Methods of determining tissues and/or cell types giving rise to cell-free dna, and methods of identifying a disease or disorder using same

ABSTRACT

The present disclosure provides methods of determining one or more tissues and/or cell-types contributing to cell-free DNA (“cfDNA”) in a biological sample of a subject. In some embodiments, the present disclosure provides a method of identifying a disease or disorder in a subject as a function of one or more determined more tissues and/or cell-types contributing to cfDNA in a biological sample from the subject.

PRIORITY CLAIM

This application is a continuation of U.S. Pat. Application No.16/880,884, filed May 21, 2020, which is a continuation of U.S. Pat.Application No. 16/160,990, filed on Oct. 15, 2018, which is acontinuation of U.S. Pat. Application No. 15/329,228, filed on Jan. 25,2017, which is a 371 national phase application of InternationalApplication No. PCT/US2015/042310, filed on Jul. 27, 2015, which claimsthe benefit of U.S. Provisional Application No. 62/029,178, filed onJul. 25, 2014 and U.S. Provisional Application No. 62/087,619 filed onDec. 4, 2014. The contents of the aforementioned applications areincorporated herein by reference.

STATEMENT OF GOVERNMENT INTEREST

This invention was made with government support under Grant Nos.1DP1HG007811 awarded by the National Institutes of Health (NIH). Thegovernment has certain rights in the invention.

SEQUENCE LISTING

This application contains a Sequence Listing, which is submitted inASCII format via USPTO EFS-Web, and is hereby incorporated by referencein its entirety. The ASCII copy, created on Apr. 20, 2022, is named2022-04-20 Parent Sequence_Listing_ST25_072227-8115US03 and is 2kilobytes in size.

TECHNICAL FIELD

The present disclosure relates to methods of determining one or moretissues and/or cell-types giving rise to cell-free DNA. In someembodiments, the present disclosure provides a method of identifying adisease or disorder in a subject as a function of one or more determinedtissues and/or cell-types associated with cell-free DNA in a biologicalsample from the subject.

BACKGROUND

Cell-free DNA (“cfDNA”) is present in the circulating plasma, urine, andother bodily fluids of humans. The cfDNA comprises double-stranded DNAfragments that are relatively short (overwhelmingly less than 200base-pairs) and are normally at a low concentration (e.g. 1-100 ng/mL inplasma). In the circulating plasma of healthy individuals, cfDNA isbelieved to primarily derive from apoptosis of blood cells (i.e., normalcells of the hematopoietic lineage). However, in specific situations,other tissues can contribute substantially to the composition of cfDNAin bodily fluids such as circulating plasma.

While cfDNA has been used in certain specialties (e.g., reproductivemedicine, cancer diagnostics, and transplant medicine), existing testsbased on cfDNA rely on differences in genotypes (e.g., primary sequenceor copy number representation of a particular sequence) between two ormore cell populations (e.g., maternal genome vs. fetal genome; normalgenome vs. cancer genome; transplant recipient genome vs. donor genome,etc.). Unfortunately, because the overwhelming majority of cfDNAfragments found in any given biological sample derive from regions ofthe genome that are identical in sequence between the contributing cellpopulations, existing cfDNA-based tests are extremely limited in theirscope of application. In addition, many diseases and disorders areaccompanied by changes in the tissues and/or cell-types giving rise tocfDNA, for example from tissue damage or inflammatory processesassociated with the disease or disorder. Existing cfDNA-based diagnostictests relying on differences in primary sequence or copy numberrepresentation of particular sequences between two genomes cannot detectsuch changes. Thus, while the potential for cfDNA to provide powerfulbiopsy-free diagnostic methods is enormous, there still remains a needfor cfDNA-based diagnostic methodologies that can be applied to diagnosea wide variety of diseases and disorders.

SUMMARY

The present disclosure provides methods of determining one or moretissues and/or cell-types giving rise to cell-free DNA (“cfDNA”) in abiological sample of a subject. In some embodiments, the presentdisclosure provides a method of identifying a disease or disorder in asubject as a function of one or more determined tissues and/orcell-types associated with cfDNA in a biological sample from thesubject.

In some embodiments, the present disclosure provides a method ofdetermining tissues and/or cell types giving rise to cell-free DNA(cfDNA) in a subject, the method comprising isolating cfDNA from abiological sample from the subject, the isolated cfDNA comprising aplurality of cfDNA fragments; determining a sequence associated with atleast a portion of the plurality of cfDNA fragments; determining agenomic location within a reference genome for at least some cfDNAfragment endpoints of the plurality of cfDNA fragments as a function ofthe cfDNA fragment sequences; and determining at least some of thetissues and/or cell types giving rise to the cfDNA fragments as afunction of the genomic locations of at least some of the cfDNA fragmentendpoints.

In other embodiments, the present disclosure provides a method ofidentifying a disease or disorder in a subject, the method comprisingisolating cell-free DNA (cfDNA) from a biological sample from thesubject, the isolated cfDNA comprising a plurality of cfDNA fragments;determining a sequence associated with at least a portion of theplurality of cfDNA fragments; determining a genomic location within areference genome for at least some cfDNA fragment endpoints of theplurality of cfDNA fragments as a function of the cfDNA fragmentsequences; determining at least some of the tissues and/or cell typesgiving rise to the cfDNA as a function of the genomic locations of atleast some of the cfDNA fragment endpoints; and identifying the diseaseor disorder as a function of the determined tissues and/or cell typesgiving rise to the cfDNA.

In other embodiments, the present disclosure provides a method fordetermining tissues and/or cell types giving rise to cell-free DNA(cfDNA) in a subject, the method comprising: (i) generating a nucleosomemap by obtaining a biological sample from the subject, isolating thecfDNA from the biological sample, and measuring distributions (a), (b)and/or (c) by library construction and massively parallel sequencing ofcfDNA; (ii) generating a reference set of nucleosome maps by obtaining abiological sample from control subjects or subjects with known disease,isolating the cfDNA from the biological sample, measuring distributions(a), (b) and/or (c) by library construction and massively parallelsequencing of cfDNA; and (iii) determining tissues and/or cell typesgiving rise to the cfDNA from the biological sample by comparing thenucleosome map derived from the cfDNA from the biological sample to thereference set of nucleosome maps; wherein (a), (b) and (c) are: (a) thedistribution of likelihoods any specific base-pair in a human genomewill appear at a terminus of a cfDNA fragment; (b) the distribution oflikelihoods that any pair of base-pairs of a human genome will appear asa pair of termini of a cfDNA fragment; and (c) the distribution oflikelihoods that any specific base-pair in a human genome will appear ina cfDNA fragment as a consequence of differential nucleosome occupancy.

In yet other embodiments, the present disclosure provides a method fordetermining tissues and/or cell types giving rise to cfDNA in a subject,the method comprising: (i) generating a nucleosome map by obtaining abiological sample from the subject, isolating the cfDNA from thebiological sample, and measuring distributions (a), (b) and/or (c) bylibrary construction and massively parallel sequencing of cfDNA; (ii)generating a reference set of nucleosome maps by obtaining a biologicalsample from control subjects or subjects with known disease, isolatingthe cfDNA from the biological sample, measuring distributions (a), (b)and/or (c) by library construction and massively parallel sequencing ofDNA derived from fragmentation of chromatin with an enzyme such asmicrococcal nuclease, DNase, or transposase; and (iii) determiningtissues and/or cell types giving rise to the cfDNA from the biologicalsample by comparing the nucleosome map derived from the cfDNA from thebiological sample to the reference set of nucleosome maps; wherein (a),(b) and (c) are: (a) the distribution of likelihoods any specificbase-pair in a human genome will appear at a terminus of a sequencedfragment; (b) the distribution of likelihoods that any pair ofbase-pairs of a human genome will appear as a pair of termini of asequenced fragment; and (c) the distribution of likelihoods that anyspecific base-pair in a human genome will appear in a sequenced fragmentas a consequence of differential nucleosome occupancy.

In other embodiments, the present disclosure provides a method fordiagnosing a clinical condition in a subject, the method comprising: (i)generating a nucleosome map by obtaining a biological sample from thesubject, isolating cfDNA from the biological sample, and measuringdistributions (a), (b) and/or (c) by library construction and massivelyparallel sequencing of cfDNA; (ii) generating a reference set ofnucleosome maps by obtaining a biological sample from control subjectsor subjects with known disease, isolating the cfDNA from the biologicalsample, measuring distributions (a), (b) and/or (c) by libraryconstruction and massively parallel sequencing of cfDNA; and (iii)determining the clinical condition by comparing the nucleosome mapderived from the cfDNA from the biological sample to the reference setof nucleosome maps; wherein (a), (b) and (c) are: (a) the distributionof likelihoods any specific base-pair in a human genome will appear at aterminus of a cfDNA fragment; (b) the distribution of likelihoods thatany pair of base-pairs of a human genome will appear as a pair oftermini of a cfDNA fragment; and (c) the distribution of likelihoodsthat any specific base-pair in a human genome will appear in a cfDNAfragment as a consequence of differential nucleosome occupancy.

In other embodiments, the present disclosure provides a method fordiagnosing a clinical condition in a subject, the method comprising (i)generating a nucleosome map by obtaining a biological sample from thesubject, isolating cfDNA from the biological sample, and measuringdistributions (a), (b) and/or (c) by library construction and massivelyparallel sequencing of cfDNA; (ii) generating a reference set ofnucleosome maps by obtaining a biological sample from control subjectsor subjects with known disease, isolating the cfDNA from the biologicalsample, measuring distributions (a), (b) and/or (c) by libraryconstruction and massively parallel sequencing of DNA derived fromfragmentation of chromatin with an enzyme such as micrococcal nuclease(MNase), DNase, or transposase; and (iii) determining thetissue-of-origin composition of the cfDNA from the biological sample bycomparing the nucleosome map derived from the cfDNA from the biologicalsample to the reference set of nucleosome maps; wherein (a), (b) and (c)are: (a) the distribution of likelihoods any specific base-pair in ahuman genome will appear at a terminus of a sequenced fragment; (b) thedistribution of likelihoods that any pair of base-pairs of a humangenome will appear as a pair of termini of a sequenced fragment; and (c)the distribution of likelihoods that any specific base-pair in a humangenome will appear in a sequenced fragment as a consequence ofdifferential nucleosome occupancy.

These and other embodiments are described in greater detail below.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

This application contains at least one drawing executed in color. Copiesof this application with color drawing(s) will be provided by the Officeupon request and payment of the necessary fees.”

FIGS. 1A-C show three types of information that relate cfDNAfragmentation patterns to nucleosome occupancy, exemplified for a smallgenomic region. These same types of information might also arise throughfragmentation of chromatin with an enzyme such as micrococcal nuclease(MNase), DNase, or transposase. FIG. 1A shows the distribution oflikelihoods any specific base-pair in a human genome will appear at aterminus of a sequenced fragment (i.e. points of fragmentation); FIG. 1Bshows the distribution of likelihoods that any pair of base-pairs of ahuman genome will appear as a pair of termini of a sequenced fragment(i.e. consecutive pairs of fragmentation points that give rise to anindividual molecule); and FIG. 1C shows the distribution of likelihoodsthat any specific base-pair in a human genome will appear within asequenced fragment (i.e. relative coverage) as a consequence ofdifferential nucleosome occupancy.

FIG. 2 shows insert size distribution of a typical cfDNA sequencinglibrary; here shown for the pooled cfDNA sample derived from humanplasma containing contributions from an unknown number of healthyindividuals (bulk.cfDNA).

FIG. 3A shows average periodogram intensities from Fast FourierTransformation (FFT) of read start coordinates mapping to the first(chr1) human autosome across all cfDNA samples (Plasma), cfDNA fromtumor patient samples (Tumor), cfDNA from pregnant female individuals(Pregnancy), MNase of human different human cell lines (Cell lines) anda human DNA shotgun sequencing library (Shotgun).

FIG. 3B shows average periodogram intensities from Fast FourierTransformation (FFT) of read start coordinates mapping to the last(chr22) human autosome across all cfDNA samples (Plasma), cfDNA fromtumor patient samples (Tumor), cfDNA from pregnant female individuals(Pregnancy), MNase of human different human cell lines (Cell lines) anda human DNA shotgun sequencing library (Shotgun).

FIGS. 4A-B show first three principal components (PC) of intensities at196 base-pairs (bp) periodicity in 10 kilobase-pair (kbp) blocks acrossall autosomes: FIG. 4A shows PC 2 vs. PC 1; FIG. 4B shows PC 3 vs. PC 2.

FIG. 5 shows hierarchical clustering dendogram of Euclidean distances ofintensities measured at 196 bp periodicity in 10 kbp blocks across allautosomes.

FIGS. 6A-B show first three principal components of intensities at 181bp to 202 bp periodicity in 10 kbp blocks across all autosomes: FIG. 6Ashows PC 2 vs. PC 1; FIG. 6B shows PC3 vs. PC 2.

FIG. 7 shows hierarchical clustering dendogram of Euclidean distances ofintensities measured at 181 bp to 202 bp periodicity in 10 kbp blocksacross all autosomes.

FIGS. 8A-F show principal component analysis (first 7 of 10 PCs) ofintensities at 181 bp to 202 bp periodicity in 10 kbp blocks across allautosomes for the cfDNA data sets: FIG. 8A shows PC 2 vs. PC 1; FIG. 8Bshows PC 3 vs. PC 2; FIG. 8C shows PC 4 vs. PC 3; FIG. 8D shows PC 5 vs.PC 4; FIG. 8E shows PC 6 vs. PC 5; FIG. 8F shows PC 7 vs. PC 6.

FIGS. 9A-E show principal component analysis of intensities at 181 bp to202 bp periodicity in 10 kbp blocks across all autosomes for the MNasedata sets: FIG. 9A shows PC 2 vs. PC 1; FIG. 9B shows PC 3 vs. PC 2;FIG. 9C shows PC 4 vs. PC 3; FIG. 9D shows PC 5 vs. PC 4; FIG. 9E showsPC 6 vs. PC 5.

FIG. 10 shows average periodogram intensities for a representative humanautosome (chr11) across all synthetic cfDNA and MNase data set mixtures:

FIG. 11 shows first two principal components of intensities at 181 bp to202 bp periodicity in 10 kbp blocks across all autosomes for thesynthetic MNase data set mixtures.

FIG. 12 shows first two principal components of intensities at 181 bp to202 bp periodicity in 10 kbp blocks across all autosomes for thesynthetic cfDNA data set mixtures.

FIG. 13 shows hierarchical clustering dendogram of Euclidean distancesof intensities at 181 bp to 202 bp periodicity in 10 kbp blocks acrossall autosomes for the synthetic MNase and cfDNA mixture data sets.

FIG. 14 shows read-start density in 1 kbp window around 23,666 CTCFbinding sites for a set of samples with at least 100 M reads.

FIG. 15 shows read-start density in 1 kbp window around 5,644 c-Junbinding sites for a set of samples with at least 100 M reads.

FIG. 16 shows read-start density for 1 kbp window around 4,417 NF-YBbinding sites for a set of samples with at least 100 M reads.

FIG. 17 shows a schematic overview of the processes giving rise to cfDNAfragments. Apoptotic and/or necrotic cell death results in near-completedigestion of native chromatin. Protein-bound DNA fragments, typicallyassociated with histones or transcription factors, preferentiallysurvive digestion and are released into the circulation, while naked DNAis lost. Fragments can be recovered from peripheral blood plasmafollowing proteinase treatment. In healthy individuals, cfDNA isprimarily derived from myeloid and lymphoid cell lineages, butcontributions from one or more additional tissues may be present incertain medical conditions.

FIG. 18 shows fragment length of cfDNA observed with conventionalsequencing library preparation. Length is inferred from alignment ofpaired-end sequencing reads. A reproducible peak in fragment length at167 base-pairs (bp) (green dashed line) is consistent with associationwith chromatosomes. Additional peaks evidence ~10.4 bp periodicity,corresponding to the helical pitch of DNA on the nucleosome core.Enzymatic end-repair during library preparation removes 5′ and 3′overhangs and may obscure true cleavage sites.

FIG. 19 shows a dinucleotide composition of 167 bp fragments andflanking genomic sequence in conventional libraries. Observeddinucleotide frequencies in the BH01 library were compared to expectedfrequencies from simulated fragments (matching for endpoint biasesresulting from both cleavage and adapter ligation preferences).

FIG. 20 shows a schematic of a single-stranded library preparationprotocol for cfDNA fragments.

FIG. 21 shows fragment length of cfDNA observed with single-strandedsequencing library preparation. No enzymatic end-repair is performed totemplate molecules during library preparation. Short fragments of 50-120bp are highly enriched compared to conventional libraries. While ~10.4bp periodicity remains, its phase is shifted by ~3 bp.

FIG. 22 shows a dinucleotide composition of 167 bp fragments andflanking genomic sequence in single-stranded libraries. Observeddinucleotide frequencies in the IH02 library were compared to expectedfrequencies derived from simulated fragments, again matching forendpoint biases. The apparent difference in the background level of biasbetween BH01 and IH02 relate to differences between the simulations,rather than the real libraries (data not shown).

FIG. 23A shows a gel image of representative cfDNA sequencing libraryprepared with the conventional protocol.

FIG. 23B shows a gel image of a representative cfDNA sequencing libraryprepared with the single-stranded protocol.

FIG. 24A shows mononucleotide cleavage biases of cfDNA fragments.

FIG. 24B shows dinucleotide cleavage biases of cfDNA fragments.

FIG. 25 shows a schematic overview of inference of nucleosomepositioning. A per-base windowed protection score (WPS) is calculated bysubtracting the number of fragment endpoints within a 120 bp window fromthe number of fragments completely spanning the window. High WPS valuesindicate increased protection of DNA from digestion; low values indicatethat DNA is unprotected. Peak calls identify contiguous regions ofelevated WPS.

FIG. 26 shows strongly positioned nucleosomes at a well-studiedalpha-satellite array. Coverage, fragment endpoints, and WPS values fromsample CH01 are shown for long fragment (120 bp window; 120-180 bpreads) or short fragment (16 bp window; 35-80 bp reads) bins at apericentromeric locus on chromosome 12. Nucleosome calls from CH01(middle, blue boxes) are regularly spaced across the locus. Nucleosomecalls based on MNase digestion from two published studies (middle,purple and black boxes) are also displayed. The locus overlaps with anannotated alpha-satellite array.

FIG. 27 shows inferred nucleosome positioning around a DNase Ihypersensitive site (DHS) on chromosome 9. Coverage, fragment endpoints,and WPS values from sample CH01 are shown for long and short fragmentbins. The hypersensitive region, highlighted in gray, is marked byreduced coverage in the long fragment bin. Nucleosome calls from CH01(middle, blue boxes) adjacent to the DHS are spaced more widely thantypical adjacent pairs, consistent with accessibility of the interveningsequence to regulatory proteins including transcription factors.Coverage of shorter fragments, which may be associated with suchproteins, is increased at the DHS, which overlaps with several annotatedtranscription factor binding sites (not shown). Nucleosome calls basedon MNase digestion from two published studies are shown as in FIG. 26 .

FIG. 28 shows a schematic of peak calling and scoring according to oneembodiment of the present disclosure.

FIG. 29 shows CH01 peak density by GC content.

FIG. 30 shows a histogram of distances between adjacent peaks by sample.Distances are measured from peak call to adjacent call.

FIG. 31 shows a comparison of peak calls between samples. For each pairof samples, the distances between each peak call in the sample withfewer peaks and the nearest peak call in the other sample are calculatedand visualized as a histogram with bin size of 1. Negative numbersindicate the nearest peak is upstream; positive numbers indicate thenearest peak is downstream.

FIGS. 32A-C show a comparison of peak calls between samples: FIG. 32Ashows IH01 vs. BH01; FIG. 32B shows IH02 vs. BH01; FIG. 32C shows IH02vs. IH01.

FIG. 33A shows nucleosome scores for real vs. simulated peaks.

FIG. 33B shows median peak offset within a score bin as a function ofthe score bin (left y-axis), and the number of peaks in each score bin(right y-axis).

FIGS. 34A-C show a comparison of peak calls between samples and matchedsimulations: FIG. 34A shows BH01 simulation vs. BH01 actual; FIG. 34Bshows IH01 simulation vs. IH01 actual; FIG. 34C shows IH02 simulationvs. IH01 actual.

FIG. 35 shows distances between adjacent peaks, sample CH01. The dottedblack line indicates the mode of the distribution (185 bp).

FIG. 36 shows aggregate, adjusted windowed protection scores (WPS; 120bp window) around 22,626 transcription start sites (TSS). TSS arealigned at the 0 position after adjusting for strand and direction oftranscription. Aggregate WPS is tabulated for both real data andsimulated data by summing per-TSS WPS at each position relative to thecentered TSS. The values plotted represent the difference between thereal and simulated aggregate WPS, further adjusted to local backgroundas described in greater detail below. Higher WPS values indicatepreferential protection from cleavage.

FIG. 37 shows aggregate, adjusted WPS around 22,626 start codons.

FIG. 38 shows aggregate, adjusted WPS around 224,910 splice donor sites.

FIG. 39 shows aggregate, adjusted WPS around 224,910 splice acceptorsites.

FIG. 40 shows aggregate, adjusted WPS around various genic features withdata from CH01, including for real data, matched simulation, and theirdifference.

FIG. 41 shows nucleosome spacing in A/B compartments. Median nucleosomespacing in non-overlapping 100 kilobase (kb) bins, each containing ~500nucleosome calls, is calculated genome-wide. A/B compartment predictionsfor GM12878, also with 100 kb resolution, are from published sources.Compartment A is associated with open chromatin and compartment B withclosed chromatin.

FIG. 42 shows nucleosome spacing and A/B compartments on chromosomes 7and 11. A/B segmentation (red and blue bars) largely recapitulateschromosomal G-banding (ideograms, gray bars). Median nucleosome spacing(black dots) is calculated in 100 kb bins and plotted above the A/Bsegmentation.

FIG. 43 shows aggregate, adjusted WPS for 93,550 CTCF sites for the long(top) and short (bottom) fractions.

FIG. 44 shows a zoomed-in view of the aggregate, adjusted WPS for shortfraction cfDNA at CTCF sites. The light red bar (and correspondingshading within the plot) indicate the position of the known 52 bp CTCFbinding motif. The dark red subsection of this bar indicates thelocation of the 17 bp motif used for the FIMO motif search.

FIG. 45 shows -1 to +1 nucleosome spacing calculated around CTCF sitesderived from clustered FIMO predicted CTCF sites (purely motif-based:518,632 sites), a subset of these predictions overlapping with ENCODEChIP-seq peaks (93,530 sites), and a further subset that have beenexperimentally observed to be active across 19 cell lines (23,723sites). The least stringent set of CTCF sites are predominantlyseparated by distances that are approximately the same as thegenome-wide average (~190 bp). However, at the highest stringency, mostCTCF sites are separated by a much wider distance (~260 bp), consistentwith active CTCF binding and repositioning of adjacent nucleosomes.

FIGS. 46-48 show CTCF occupancy repositions flanking nucleosomes: FIG.46 shows inter-peak distances for the three closest upstream and threeclosest downstream peak calls for 518,632 CTCF binding sites predictedby FIMO. FIG. 47 shows inter-peak distances for the three closestupstream and three closest downstream peak calls for 518,632 CTCFbinding sites predicted by FIMO as in FIG. 46 , but where the same setof CTCF sites has been filtered based on overlap with ENCODE ChIP-seqpeaks, leaving 93,530 sites. FIG. 48 shows inter-peak distances for thethree closest upstream and three closest downstream peak calls for93,530 CTCF binding sites predicted by FIMO as in FIG. 47 , but wherethe set of CTCF sites has been filtered based on overlap with the set ofactive CTCF sites experimentally observed across 19 cell lines, leaving23,732 sites.

FIG. 49 shows, for the subset of putative CTCF sites with flankingnucleosomes spaced widely (230-270 bp), that both the long (top) andshort (bottom) fractions exhibit a stronger signal of positioning withincreasingly stringent subsets of CTCF sites. See FIG. 45 for keydefining colored lines.

FIGS. 50-52 show CTCF occupancy repositions flanking nucleosomes: FIG.50 shows mean short fraction WPS (top panel) and mean long fraction WPS(bottom panel) for the 518,632 sites, partitioned into distance binsdenoting the number of base-pairs separating the flanking +1 and -1nucleosome calls for each site. FIG. 51 shows mean short fraction WPS(top panel) and mean long fraction WPS (bottom panel) for the 518,632sites of FIG. 50 , but where the same set of CTCF sites has beenfiltered based on overlap with ENCODE ChIP-seq peaks. FIG. 52 shows meanshort fraction WPS (top panel) and mean long fraction WPS (bottom panel)for the sites of FIG. 51 , but where the same set of sites has beenfurther filtered based on overlap with the set of active CTCF sitesexperimentally observed across 19 cell lines. Key defining colored linesfor FIG. 50 is the same as in FIG. 51 and FIG. 52 .

FIGS. 53A-H show footprints of transcription factor binding sites fromshort and long cfDNA fragments. Clustered FIMO binding sites predictionswere intersected with ENCODE ChIP-seq data to obtain a confident set oftranscription factor (TF) binding sites for a set of additional factors.Aggregate, adjusted WPS for regions flanking the resulting sets of TFbinding sites is displayed for both the long and short fractions ofcfDNA fragments. Higher WPS values indicate higher likelihood ofnucleosome or TF occupancy, respectively. FIG. 53A: AP-2; FIG. 53B:E2F-2; FIG. 53C: EBOX-TF; FIG. 53D: IRF; FIG. 53E: MYC-MAX; FIG. 53F:PAX5-2; FIG. 53G: RUNX-AML; FIG. 53H: YY1.

FIG. 54 shows aggregate, adjusted WPS for transcription factor ETS(210,798 sites). WPS calculated from both long (top) and short (bottom)cfDNA fractions are shown. Signal consistent with TF protection at thebinding site itself (short fraction) with organization of thesurrounding nucleosomes (long fraction) is observed. Similar analysesfor additional TFs are shown in FIGS. 53A-H.

FIG. 55 shows aggregate, adjusted WPS for transcription factor MAFK(32,159 sites). WPS calculated from both long (top) and short (bottom)cfDNA fractions are shown. Signal consistent with TF protection at thebinding site itself (short fraction) with organization of thesurrounding nucleosomes (long fraction) is observed. Similar analysesfor additional TFs are shown in FIGS. 53A-H.

FIG. 56 shows the inference of mixtures of cell-types contributing tocell-free DNA based on DNase hypersensitivity (DHS) sites. The frequencydistribution of peak-to-peak spacing of nucleosome calls at DHS sitesfrom 116 diverse biological samples shows a bimodal distribution, withthe second mode plausibly corresponding to widened nucleosome spacing atactive DHS sites due to intervening transcription factor binding (~190bp → 260 bp). DHS sites identified in lymphoid or myeloid samples havethe largest proportions of DHS sites with widened nucleosome spacing,consistent with hematopoietic cell death as the dominant source of cfDNAin healthy individuals.

FIG. 57 shows how partitioning of adjusted WPS scores aroundtranscriptional start sites (TSS) into five gene expression bins(quintiles) defined for NB-4 (an acute promyelocytic leukemia cell line)reveals differences in the spacing and placement of nucleosomes. Highlyexpressed genes show a strong phasing of nucleosomes within thetranscript body. Upstream of the TSS, -1 nucleosomes are well-positionedacross expression bins, but -2 and -3 nucleosomes are onlywell-positioned for medium to highly expressed genes.

FIG. 58 shows that, for medium to highly expressed genes, a shortfragment peak is observed between the TSS and the -1 nucleosome,consistent with footprinting of the transcription preinitiation complex,or some component thereof, at transcriptionally active genes.

FIG. 59 shows that median nucleosome distance in the transcript body isnegatively correlated with gene expression as measured for the NB-4 cellline (ρ = -0.17, n = 19,677 genes). Genes with little-to-no expressionshow a median nucleosome distance of 193 bp, while for expressed genes,this ranges between 186-193 bp. This negative correlation is strongerwhen more nucleosome calls are used to determine a more precise mediandistance (e.g. requiring at least 60 nucleosomes, ρ = -0.50; n = 12,344genes).

FIG. 60 shows how, to deconvolve multiple contributions, fast Fouriertransformation (FFT) was used to quantify the abundance of specificfrequency contributions (intensities) in the long fragment WPS for thefirst 10 kb of gene bodies starting at each TSS. Shown are trajectoriesof correlation between RNA expression in 76 cell lines and primarytissues with these intensities at different frequencies. Marked with abold black line is the NB-4 cell line. Correlations are strongest inmagnitude for intensities in the 193-199 bp frequency range.

FIG. 61 shows the inference of cell-types contributing to cell-free DNAin healthy states and cancer. The top panel shows the ranks ofcorrelation for 76 RNA expression datasets with average intensity in the193-199 bp frequency range for various cfDNA libraries, categorized bytype and listed from highest rank (top rows) to lowest rank (bottomrows). Correlation values and full cell line or tissue names areprovided in Table 3. All of the strongest correlations for all threehealthy samples (BH01, IH01 and IH02; first three columns) are withlymphoid and myeloid cell lines as well as bone marrow. In contrast,cfDNA samples obtained from stage IV cancer patients (IC15, IC17, IC20,IC35, IC37; last five columns) show top correlations with various cancercell lines, e.g. IC17 (hepatocellular carcinoma, HCC) showing highestcorrelations with HepG2 (hepatocellular carcinoma cell line), and IC35(breast ductal carcinoma, DC) with MCF7 (metastatic breastadenocarcinoma cell line). When comparing cell line/tissue ranksobserved for the cancer samples to each of the three healthy samples andaveraging the rank changes (bottom panel), maximum rank changes are morethan 2x higher than those observed from comparing the three healthysamples with each other and averaging rank changes (‘Control’). Forexample, for IC15 (small cell lung carcinoma, SCLC) the rank of SCLC-21H(small cell lung carcinoma cell line) increased by an average of 31positions, for IC20 (squamous cell lung carcinoma, SCC) SK-BR-3(metastatic breast adenocarcinoma cell line) increased by an averagerank of 21, and for IC37 (colorectal adenocarcinoma, AC) HepG2 increasedby 24 ranks.

FIGS. 62A-B show quantitation of aneuploidy to select samples with highburden of circulating tumor DNA, based on coverage (FIG. 62A) or allelebalance (FIG. 62B). FIG. 62A shows the sums of Z scores for eachchromosome calculated based on observed vs. expected numbers ofsequencing reads for each sample (black dots) compared to simulatedsamples that assume no aneuploidy (red dots). FIG. 62B shows the allelebalance at each of 48,800 common SNPs, evaluated per chromosome, for asubset of samples that were selected for additional sequencing.

FIGS. 63A-H show a comparison of peak calls to published nucleosome callsets: FIG. 63A shows the distance between nucleosome peak calls acrossthree published data sets (Gaffney et al. 2012, JS Pedersen et al. 2014,and A Schep et al. 2015) as well as the calls generated here, includingthe matched simulation of CA01. Previously published data sets do notshow one defined mode at the canonical ~185 bp nucleosome distance,probably due to their sparse sampling or wide call ranges. In contrast,all the nucleosome calls from cfDNA show one well-defined mode. Thematched simulated data set has shorter mode (166 bp) and a widerdistribution. Further, the higher the coverage of the cfDNA data setused to generate calls, the higher the proportion of calls representedby the mode of the distribution. FIG. 63B shows the number ofnucleosomes for each of the same list of sets as FIG. 63A. The cfDNAnucleosome calls present the most comprehensive call set with nearly 13M nucleosome peak calls. FIG. 63C shows the distances between each peakcall in the IH01 cfDNA sample and the nearest peak call from threepreviously published data sets. FIG. 63D shows the distances betweeneach peak call in the IH02 cfDNA sample and the nearest peak call fromthree previously published data sets. FIG. 63E shows the distancesbetween each peak call in the BH01 cfDNA sample and the nearest peakcall from three previously published data sets. FIG. 63F shows thedistances between each peak call in the CH01 cfDNA sample and thenearest peak call from three previously published data sets. FIG. 63Gshows the distances between each peak call in the CA01 cfDNA sample andthe nearest peak call from three previously published data sets.Negative numbers indicate the nearest peak is upstream; positive numbersindicate the nearest peak is downstream. With increased cfDNA coverage,a higher proportion of previously published calls are found in closerproximity to the determined nucleosome call. Highest concordance wasfound with calls generated by Gaffney et al., PLoS Genet., vol. 8,e1003036 (2012) and A Schep et al. (2015). FIG. 63H shows the distancesbetween each peak call and the nearest peak call from three previouslypublished data sets, but this time for the matched simulation of CA01.The closest real nucleosome positions tend to be away from the peakscalled in the simulation for the Gaffney et al., PLoS Genet., vol. 8,e1003036 (2012) and JS Pedersen et al., Genome Research, vol. 24, pp.454-466 (2014) calls. Calls generated by A Schep et al. (2015) seem toshow some overlap with the simulated calls.

DETAILED DESCRIPTION

The present disclosure provides methods of determining one or moretissues and/or cell-types giving rise to cell-free DNA in a subject’sbiological sample. In some embodiments, the present disclosure providesa method of identifying a disease or disorder in a subject as a functionof one or more determined tissues and/or cell-types associated withcfDNA in a biological sample from the subject.

The present disclosure is based on a prediction that cfDNA moleculesoriginating from different cell types or tissues differ with respect to:(a) the distribution of likelihoods any specific base-pair in a humangenome will appear at a terminus of a cfDNA fragment (i.e. points offragmentation); (b) the distribution of likelihoods that any pair ofbase-pairs of a human genome will appear as a pair of termini of a cfDNAfragment (i.e. consecutive pairs of fragmentation points that give riseto an individual cfDNA molecule); and (c) the distribution oflikelihoods that any specific base-pair in a human genome will appear ina cfDNA fragment (i.e. relative coverage) as a consequence ofdifferential nucleosome occupancy. These are referred to below asdistributions (a), (b) and (c), or collectively referred to as“nucleosome dependent cleavage probability maps”, “cleavageaccessibility maps” or “nucleosome maps” (FIG. 1 ). Of note, nucleosomemaps might also be measured through the sequencing of fragments derivedfrom the fragmentation of chromatin with an enzyme such as micrococcalnuclease (MNase), DNase, or transposase, or equivalent procedures thatpreferentially fragment genomic DNA between or at the boundaries ofnucleosomes or chromatosomes.

In healthy individuals, cfDNA overwhelmingly derives from apoptosis ofblood cells, i.e. cells of the hematopoietic lineage. As these cellsundergo programmed cell death, their genomic DNA is cleaved and releasedinto circulation, where it continues to be degraded by nucleases. Thelength distribution of cfDNA oscillates with a period of approximately10.5 base-pairs (bp), corresponding to the helical pitch of DNA coiledaround the nucleosome, and has a marked peak around 167 bp,corresponding to the length of DNA associated with a linker-associatedmononucleosome (FIG. 2 ). This evidence has led to the hypothesis thatcfDNA’s association with the nucleosome is what protects it fromcomplete, rapid degradation in the circulation. An alternativepossibility is that the length distribution arises simply from thepattern of DNA cleavage during apoptosis itself, which is influenceddirectly by nucleosome positioning. Regardless, the length distributionof cfDNA provides clear evidence that the fragmentation processes thatgive rise to cfDNA are influenced by nucleosome positioning.

In some embodiments, the present disclosure defines a nucleosome map asthe measurement of distributions (a), (b) and/or (c) by libraryconstruction and massively parallel sequencing of either cfDNA from abodily fluid or DNA derived from the fragmentation of chromatin with anenzyme such as micrococcal nuclease (MNase), DNase, or transposase, orequivalent procedures that preferentially fragment genomic DNA betweenor at the boundaries of nucleosomes or chromatosomes.. As describedbelow, these distributions may be ‘transformed’ in order to aggregate orsummarize the periodic signal of nucleosome positioning within varioussubsets of the genome, e.g. quantifying periodicity in contiguouswindows or, alternatively, in discontiguous subsets of the genomedefined by transcription factor binding sites, gene model features (e.g.transcription start sites or gene bodies), topologically associateddomains, tissue expression data or other correlates of nucleosomepositioning. Furthermore, these might be defined by tissue-specificdata. For example, one could aggregate or summarize signal in thevicinity of tissue-specific DNase I hypersensitive sites.

The present disclosure provides a dense, genome-wide map of in vivonucleosome protection inferred from plasma-borne cfDNA fragments. TheCH01 map, derived from cfDNA of healthy individuals, comprises nearly 13M uniformly spaced local maxima of nucleosome protection that span thevast majority of the mappable human reference genome. Although thenumber of peaks is essentially saturated in CH01, other metrics ofquality continued to be a function of sequencing depth (FIGS. 33A-B). Anadditional genome-wide nucleosome map was therefore constructed-byidentical methods—that is based on nearly all of the cfDNA sequencingthat the inventors have performed to date, for this study and other work(‘CA01’, 14.5 billion (G) fragments; 700-fold coverage; 13.0 M peaks).Although this map exhibits even more uniform spacing and more highlysupported peak calls (FIGS. 33A-B, 63A-H), we caution that it is basedon cfDNA from both healthy and non-healthy individuals (Tables 1, 5).

The dense, genome-wide map of nucleosome protection disclosed hereinapproaches saturation of the mappable portion of the human referencegenome, with peak-to-peak spacing that is considerably more uniform andconsistent with the expected nucleosome repeat length than previousefforts to generate human genome-wide maps of nucleosome positioning orprotection (FIGS. 63A-H). In contrast with nearly all previous efforts,the fragments that observed herein are generated by endogenousphysiological processes, and are therefore less likely to be subject tothe technical variation associated with in vitro micrococcal nucleasedigestion. The cell types that give rise to cfDNA considered in thisreference map are inevitably heterogeneous (e.g. a mixture of lymphoidand myeloid cell types in healthy individuals). Nonetheless, the map’srelative completeness may facilitate a deeper understanding of theprocesses that dictate nucleosome positioning and spacing in humancells, as well as the interplay of nucleosomes with epigeneticregulation, transcriptional output, and nuclear architecture.

Methods of Determining the Source(s) of cfDNA in a Subject’s BiologicalSample

As discussed generally above, and as demonstrated more specifically inthe Examples which follow, the present technology may be used todetermine (e.g., predict) the tissue(s) and/or cell type(s) whichcontribute to the cfDNA in a subject’s biological sample.

Accordingly, in some embodiments, the present disclosure provides amethod of determining tissues and/or cell-types giving rise to cell-freeDNA (cfDNA) in a subject, the method comprising isolating cfDNA from abiological sample from the subject, the isolated cfDNA comprising aplurality of cfDNA fragments; determining a sequence associated with atleast a portion of the plurality of cfDNA fragments; determining agenomic location within a reference genome for at least some cfDNAfragment endpoints of the plurality of cfDNA fragments as a function ofthe cfDNA fragment sequences; and determining at least some of thetissues and/or cell types giving rise to the cfDNA fragments as afunction of the genomic locations of at least some of the cfDNA fragmentendpoints.

In some embodiments, the biological sample comprises, consistsessentially of, or consists of whole blood, peripheral blood plasma,urine, or cerebral spinal fluid.

In some embodiments, the step of determining at least some of thetissues and/or cell-types giving rise to the cfDNA fragments comprisescomparing the genomic locations of at least some of the cfDNA fragmentendpoints, or mathematical transformations of their distribution, to oneor more reference maps. As used herein, the term “reference map” refersto any type or form of data which can be correlated or compared to anattribute of the cfDNA in the subject’s biological sample as a functionof the coordinate within the genome to which cfDNA sequences are aligned(e.g., the reference genome). The reference map may be correlated orcompared to an attribute of the cfDNA in the subject’s biological sampleby any suitable means. For example and without limitation, thecorrelation or comparison may be accomplished by analyzing frequenciesof cfDNA endpoints, either directly or after performing a mathematicaltransformation on their distribution across windows within the referencegenome, in the subject’s biological sample in view of numerical valuesor any other states defined for equivalent coordinates of the referencegenome by the reference map. In another non-limiting example, thecorrelation or comparison may be accomplished by analyzing thedetermined nucleosome spacing(s) based on the cfDNA of the subject’sbiological sample in view of the determined nucleosome spacing(s), oranother property that correlates with nucleosome spacing(s), in thereference map.

The reference map(s) may be sourced or derived from any suitable datasource including, for example, public databases of genomic information,published data, or data generated for a specific population of referencesubjects which may each have a common attribute (e.g., disease status).In some embodiments, the reference map comprises a DNase Ihypersensitivity dataset. In some embodiments, the reference mapcomprises an RNA expression dataset. In some embodiments, the referencemap comprises a chromosome conformation map. In some embodiments, thereference map comprises a chromatin accessibility map. In someembodiments, the reference map comprises data that is generated from atleast one tissue or cell-type that is associated with a disease or adisorder. In some embodiments, the reference map comprises positions ofnucleosomes and/or chromatosomes in a tissue or cell type. In someembodiments, the reference map is generated by a procedure that includesdigesting chromatin with an exogenous nuclease (e.g., micrococcalnuclease). In some embodiments, the reference map comprises chromatinaccessibility data determined by a transposition-based method (e.g.,ATAC-seq). In some embodiments, the reference map comprises dataassociated with positions of a DNA binding and/or DNA occupying proteinfor a tissue or cell type. In some embodiments, the DNA binding and/orDNA occupying protein is a transcription factor. In some embodiments,the positions are determined by a procedure that includes chromatinimmunoprecipitation of a crosslinked DNA-protein complex. In someembodiments, the positions are determined by a procedure that includestreating DNA associated with the tissue or cell type with a nuclease(e.g., DNase-I). In some embodiments, the reference map is generated bysequencing of cfDNA fragments from a biological sample from one or moreindividuals with a known disease. In some embodiments, this biologicalsample from which the reference map is generated is collected from ananimal to which human cells or tissues have been xenografted.

In some embodiments, the reference map comprises a biological featurecorresponding to positions of a DNA binding or DNA occupying protein fora tissue or cell type. In some embodiments, the reference map comprisesa biological feature corresponding to quantitative RNA expression of oneor more genes. In some embodiments, the reference map comprises abiological feature corresponding to the presence or absence of one ormore histone marks. In some embodiments, the reference map comprises abiological feature corresponding to hypersensitivity to nucleasecleavage.

The step of comparing the genomic locations of at least some of thecfDNA fragment endpoints to one or more reference maps may beaccomplished in a variety of ways. In some embodiments, the cfDNA datagenerated from the biological sample (e.g., the genomic locations of thecfDNA fragments, their endpoints, the frequencies of their endpoints,and/or nucleosome spacing(s) inferred from their distribution) iscompared to more than one reference map. In such embodiments, thetissues or cell-types associated with the reference maps which correlatemost highly with the cfDNA data in the biological sample are deemed tobe contributing. For example and without limitation, if the cfDNA dataincludes a list of likely cfDNA endpoints and their locations within thereference genome, the reference map(s) having the most similar list ofcfDNA endpoints and their locations within the reference genome may bedeemed to be contributing. As another non-limiting example, thereference map(s) having the most correlation (or increased correlation,relative to cfDNA from a healthy subject) with a mathematicaltransformation of the distribution of cfDNA fragment endpoints from thebiological sample may be deemed to be contributing. The tissue typesand/or cell types which correspond to those reference maps deemed to becontributing are then considered as potential sources of the cfDNAisolated from the biological sample.

In some embodiments, the step of determining at least some of thetissues and/or cell types giving rise to the cfDNA fragments comprisesperforming a mathematical transformation on a distribution of thegenomic locations of at least some of the cfDNA fragment endpoints. Onenon-limiting example of a mathematical transformation suitable for usein connection with the present technology is a Fourier transformation,such as a fast Fourier transformation (“FFT”).

In some embodiments, the method further comprises determining a scorefor each of at least some coordinates of the reference genome, whereinthe score is determined as a function of at least the plurality of cfDNAfragment endpoints and their genomic locations, and wherein the step ofdetermining at least some of the tissues and/or cell types giving riseto the observed cfDNA fragments comprises comparing the scores to one ormore reference map. The score may be any metric (e.g., a numericalranking or probability) which may be used to assign relative or absolutevalues to a coordinate of the reference genome. For example, the scoremay consist of, or be related to a probability, such as a probabilitythat the coordinate represents a location of a cfDNA fragment endpoint,or a probability that the coordinate represents a location of the genomethat is preferentially protected from nuclease cleavage by nucleosome orprotein binding. As another example, the score may relate to nucleosomespacing in particular regions of the genome, as determined by amathematical transformation of the distribution of cfDNA fragmentendpoints within that region. Such scores may be assigned to thecoordinate by any suitable means including, for example, by countingabsolute or relative events (e.g., the number of cfDNA fragmentendpoints) associated with that particular coordinate, or performing amathematical transformation on the values of such counts in the regionor a genomic coordinate. In some embodiments, the score for a coordinateis related to the probability that the coordinate is a location of acfDNA fragment endpoint. In other embodiments, the score for acoordinate is related to the probability that the coordinate representsa location of the genome that is preferentially protected from nucleasecleavage by nucleosome or protein binding. In some embodiments, thescore is related to nucleosome spacing in the genomic region of thecoordinate.

The tissue(s) and/or cell-type(s) referred to in the methods describedherein may be any tissue or cell-type which gives rise to cfDNA. In someembodiments, the tissue or cell-type is a primary tissue from a subjecthaving a disease or disorder. In some embodiments, the disease ordisorder is selected from the group consisting of: cancer, normalpregnancy, a complication of pregnancy (e.g., aneuploid pregnancy),myocardial infarction, inflammatory bowel disease, systemic autoimmunedisease, localized autoimmune disease, allotransplantation withrejection, allotransplantation without rejection, stroke, and localizedtissue damage.

In some embodiments, the tissue or cell type is a primary tissue from ahealthy subject.

In some embodiments, the tissue or cell type is an immortalized cellline.

In some embodiments, the tissue or cell type is a biopsy from a tumor.

In some embodiments, the reference map is based on sequence dataobtained from samples obtained from at least one reference subject. Insome embodiments, this sequence data defines positions of cfDNA fragmentendpoints within a reference genome - for example, if the reference mapis generated by sequencing of cfDNA from subject(s) with known disease.In other embodiments, this sequence data on which the reference map isbased may comprise any one or more of: a DNase I hypersensitive sitedataset, an RNA expression dataset, a chromosome conformation map, or achromatin accessibility map, or nucleosome positioning map generated bydigestion of chromatin with micrococcal nuclease.

In some embodiments, the reference subject is healthy. In someembodiments, the reference subject has a disease or disorder, optionallyselected from the group consisting of: cancer, normal pregnancy, acomplication of pregnancy (e.g., aneuploid pregnancy), myocardialinfarction, inflammatory bowel disease, systemic autoimmune disease,localized autoimmune disease, allotransplantation with rejection,allotransplantation without rejection, stroke, and localized tissuedamage.

In some embodiments, the reference map comprises scores for at least aportion of coordinates of the reference genome associated with thetissue or cell type. In some embodiments, the reference map comprises amathematical transformation of the scores, such as a Fouriertransformation of the scores. In some embodiments, the scores are basedon annotations of reference genomic coordinates for the tissue or celltype. In some embodiments, the scores are based on positions ofnucleosomes and/or chromatosomes. In some embodiments, the scores arebased on transcription start sites and/or transcription end sites. Insome embodiments, the scores are based on predicted binding sites of atleast one transcription factor. In some embodiments, the scores arebased on predicted nuclease hypersensitive sites. In some embodiments,the scores are based on predicted nucleosome spacing.

In some embodiments, the scores are associated with at least oneorthogonal biological feature. In some embodiments, the orthogonalbiological feature is associated with highly expressed genes. In someembodiments, the orthogonal biological feature is associated with lowlyexpression genes.

In some embodiments, at least some of the plurality of the scores has avalue above a threshold (minimum) value. In such embodiments, scoresfalling below the threshold (minimum) value are excluded from the stepof comparing the scores to a reference map. In some embodiments, thethreshold value is determined before determining the tissue(s) and/orthe cell type(s) giving rise to the cfDNA. In other embodiments, thethreshold value is determined after determining the tissue(s) and/or thecell type(s) giving rise to the cfDNA.

In some embodiments, the step of determining the tissues and/or celltypes giving rise to the cfDNA as a function of a plurality of thegenomic locations of at least some of the cfDNA fragment endpointscomprises comparing a mathematical transformation of the distribution ofthe genomic locations of at least some of the cfDNA fragment endpointsof the sample with one or more features of one or more reference maps.One non-limiting example of a mathematical transformation suitable forthis purpose is a Fourier transformation, such as a fast Fouriertransformation (“FFT”).

In any embodiment described herein, the method may further comprisegenerating a report comprising a list of the determined tissues and/orcell-types giving rise to the isolated cfDNA. The report may optionallyfurther include any other information about the sample and/or thesubject, the type of biological sample, the date the biological samplewas obtained from the subject, the date the cfDNA isolation step wasperformed and/or tissue(s) and/or cell-type(s) which likely did not giverise to any cfDNA isolated from the biological sample.

In some embodiments, the report further includes a recommended treatmentprotocol including, for example and without limitation, a suggestion toobtain an additional diagnostic test from the subject, a suggestion tobegin a therapeutic regimen, a suggestion to modify an existingtherapeutic regimen with the subject, and/or a suggestion to suspend orstop an existing therapeutic regiment.

Methods of Identifying a Disease or Disorder in a Subject

As discussed generally above, and as demonstrated more specifically inthe Examples which follow, the present technology may be used todetermine (e.g., predict) a disease or disorder, or the absence of adisease or a disorder, based at least in part on the tissue(s) and/orcell type(s) which contribute to cfDNA in a subject’s biological sample.

Accordingly, in some embodiments, the present disclosure provides amethod of identifying a disease or disorder in a subject, the methodcomprising isolating cell free DNA (cfDNA) from a biological sample fromthe subject, the isolated cfDNA comprising a plurality of cfDNAfragments; determining a sequence associated with at least a portion ofthe plurality of cfDNA fragments; determining a genomic location withina reference genome for at least some cfDNA fragment endpoints of theplurality of cfDNA fragments as a function of the cfDNA fragmentsequences; determining at least some of the tissues and/or cell typesgiving rise to the cfDNA as a function of the genomic locations of atleast some of the cfDNA fragment endpoints; and identifying the diseaseor disorder as a function of the determined tissues and/or cell typesgiving rise to the cfDNA.

In some embodiments, the biological sample comprises, consistsessentially of, or consists of whole blood, peripheral blood plasma,urine, or cerebral spinal fluid.

In some embodiments, the step of determining the tissues and/orcell-types giving rise to the cfDNA comprises comparing the genomiclocations of at least some of the cfDNA fragment endpoints, ormathematical transformations of their distribution, to one or morereference maps. The term “reference map” as used in connection withthese embodiments may have the same meaning described above with respectto methods of determining tissue(s) and/or cell type(s) giving rise tocfDNA in a subject’s biological sample. In some embodiments, thereference map may comprise any one or more of: a DNase I hypersensitivesite dataset, an RNA expression dataset, a chromosome conformation map,a chromatin accessibility map, sequence data that is generated fromsamples obtained from at least one reference subject, enzyme-mediatedfragmentation data corresponding to at least one tissue that isassociated with a disease or a disorder, and/or positions of nucleosomesand/or chromatosomes in a tissue or cell type. In some embodiments, thereference map is generated by sequencing of cfDNA fragments from abiological sample from one or more individuals with a known disease. Insome embodiments, this biological sample from which the reference map isgenerated is collected from an animal to which human cells or tissueshave been xenografted.

In some embodiments, the reference map is generated by digestingchromatin with an exogenous nuclease (e.g., micrococcal nuclease). Insome embodiments, the reference maps comprise chromatin accessibilitydata determined by a transposition-based method (e.g., ATAC-seq). Insome embodiments, the reference maps comprise data associated withpositions of a DNA binding and/or DNA occupying protein for a tissue orcell type. In some embodiments, the DNA binding and/or DNA occupyingprotein is a transcription factor. In some embodiments, the positionsare determined chromatin immunoprecipitation of a crosslinkedDNA-protein complex. In some embodiments, the positions are determinedby treating DNA associated with the tissue or cell type with a nuclease(e.g., DNase-I).

In some embodiments, the reference map comprises a biological featurecorresponding to positions of a DNA binding or DNA occupying protein fora tissue or cell type. In some embodiments, the reference map comprisesa biological feature corresponding to quantitative expression of one ormore genes. In some embodiments, the reference map comprises abiological feature corresponding to the presence or absence of one ormore histone marks. In some embodiments, the reference map comprises abiological feature corresponding to hypersensitivity to nucleasecleavage.

In some embodiments, the step of determining the tissues and/or celltypes giving rise to the cfDNA comprises performing a mathematicaltransformation on a distribution of the genomic locations of at leastsome of the plurality of the cfDNA fragment endpoints. In someembodiments, the mathematical transformation includes a Fouriertransformation.

In some embodiments, the method further comprises determining a scorefor each of at least some coordinates of the reference genome, whereinthe score is determined as a function of at least the plurality of cfDNAfragment endpoints and their genomic locations, and wherein the step ofdetermining at least some of the tissues and/or cell types giving riseto the observed cfDNA fragments comprises comparing the scores to one ormore reference maps. The score may be any metric (e.g., a numericalranking or probability) which may be used to assign relative or absolutevalues to a coordinate of the reference genome. For example, the scoremay consist of, or be related to a probability, such as a probabilitythat the coordinate represents a location of a cfDNA fragment endpoint,or a probability that the coordinate represents a location of the genomethat is preferentially protected from nuclease cleavage by nucleosome orprotein binding. As another example, the score may relate to nucleosomespacing in particular regions of the genome, as determined by amathematical transformation of the distribution of cfDNA fragmentendpoints within that region. Such scores may be assigned to thecoordinate by any suitable means including, for example, by countingabsolute or relative events (e.g., the number of cfDNA fragmentendpoints) associated with that particular coordinate, or performing amathematical transformation on the values of such counts in the regionor a genomic coordinate. In some embodiments, the score for a coordinateis related to the probability that the coordinate is a location of acfDNA fragment endpoint. In other embodiments, the score for acoordinate is related to the probability that the coordinate representsa location of the genome that is preferentially protected from nucleasecleavage by nucleosome or protein binding. In some embodiments, thescore is related to nucleosome spacing in the genomic region of thecoordinate.

The term “score” as used in connection with these embodiments may havethe same meaning described above with respect to methods of determiningtissue(s) and/or cell type(s) giving rise to cfDNA in a subject’sbiological sample. In some embodiments, the score for a coordinate isrelated to the probability that the coordinate is a location of a cfDNAfragment endpoint. In other embodiments, the score for a coordinate isrelated to the probability that the coordinate represents a location ofthe genome that is preferentially protected from nuclease cleavage bynucleosome or protein binding. In some embodiments, the score is relatedto nucleosome spacing in the genomic region of the coordinate.

In some embodiments, the tissue or cell-type used for generating areference map is a primary tissue from a subject having a disease ordisorder. In some embodiments, the disease or disorder is selected fromthe group consisting of: cancer, normal pregnancy, a complication ofpregnancy (e.g., aneuploid pregnancy), myocardial infarction, systemicautoimmune disease, localized autoimmune disease, inflammatory boweldisease, allotransplantation with rejection, allotransplantation withoutrejection, stroke, and localized tissue damage.

In some embodiments, the tissue or cell type is a primary tissue from ahealthy subject.

In some embodiments, the tissue or cell type is an immortalized cellline.

In some embodiments, the tissue or cell type is a biopsy from a tumor.

In some embodiments, the reference map is based on sequence dataobtained from samples obtained from at least one reference subject. Insome embodiments, this sequence data defines positions of cfDNA fragmentendpoints within a reference genome - for example, if the reference mapis generated by sequencing of cfDNA from subject(s) with known disease.In other embodiments, this sequence data on which the reference map isbased may comprise any one or more of: a DNase I hypersensitive sitedataset, an RNA expression dataset, a chromosome conformation map, or achromatin accessibility map, or nucleosome positioning map generated bydigestion with micrococcal nuclease. In some embodiments, the referencesubject is healthy. In some embodiments, the reference subject has adisease or disorder. In some embodiments, the disease or disorder isselected from the group consisting of: cancer, normal pregnancy, acomplication of pregnancy (e.g., aneuploid pregnancy), myocardialinfarction, systemic autoimmune disease, inflammatory bowel disease,localized autoimmune disease, allotransplantation with rejection,allotransplantation without rejection, stroke, and localized tissuedamage.

In some embodiments, the reference map comprises cfDNA fragment endpointprobabilities, or a quantity that correlates with such probabilities,for at least a portion of the reference genome associated with thetissue or cell type. In some embodiments, the reference map comprises amathematical transformation of the cfDNA fragment endpointprobabilities, or a quantity that correlates with such probabilities.

In some embodiments, the reference map comprises scores for at least aportion of coordinates of the reference genome associated with thetissue or cell type. In some embodiments, the reference map comprises amathematical transformation of the scores, such as a Fouriertransformation of the scores. In some embodiments, the scores are basedon annotations of reference genomic coordinates for the tissue or celltype. In some embodiments, the scores are based on positions ofnucleosomes and/or chromatosomes. In some embodiments, the scores arebased on transcription start sites and/or transcription end sites. Insome embodiments, the scores are based on predicted binding sites of atleast one transcription factor. In some embodiments, the scores arebased on predicted nuclease hypersensitive sites.

In some embodiments, the scores are associated with at least oneorthogonal biological feature. In some embodiments, the orthogonalbiological feature is associated with highly expressed genes. In someembodiments, the orthogonal biological feature is associated with lowlyexpression genes.

In some embodiments, at least some of the plurality of the scores eachhas a score above a threshold value. In such embodiments, scores fallingbelow the threshold (minimum) value are excluded from the step ofcomparing the scores to a reference map. In some embodiments, thethreshold value is determined before determining the tissue(s) and/orthe cell type(s) giving rise to the cfDNA. In other embodiments, thethreshold value is determined after determining the tissue(s) and/or thecell type(s) giving rise to the cfDNA.

In some embodiments, the step of determining the tissues and/or celltypes giving rise to the cfDNA as a function of a plurality of thegenomic locations of at least some of the cfDNA fragment endpointscomprises a mathematical transformation of the distribution of thegenomic locations of at least some of the cfDNA fragment endpoints ofthe sample with one or more features of one or more reference maps.

In some embodiments, this mathematical transformation includes a Fouriertransformation.

In some embodiments, the reference map comprises enzyme-mediatedfragmentation data corresponding to at least one tissue that isassociated with the disease or disorder.

In some embodiments, the reference genome is associated with a human.

In one aspect of the invention, the methods described herein are usedfor detection, monitoring and tissue(s) and/or cell-type(s)-of-originassessment of malignancies from analysis of cfDNA in bodily fluids. Itis now well documented that in patients with malignancies, a portion ofcfDNA in bodily fluids such as circulating plasma can be derived fromthe tumor. The methods described here can potentially be used to detectand quantify this tumor derived portion. Furthermore, as nucleosomeoccupancy maps are cell-type specific, the methods described here canpotentially be used to determine the tissue(s) and/orcell-type(s)-of-origin of a malignancy. Also, as noted above, it hasbeen observed that there is a major increase in the concentration ofcirculating plasma cfDNA in cancer, potentially disproportionate to thecontribution from the tumor itself. This suggests that other tissues(e.g. stromal, immune system) may possibly be contributing tocirculating plasma cfDNA during cancer. To the extent that contributionsfrom such other tissues to cfDNA are consistent between patients for agiven type of cancer, the methods described above may enable cancerdetection, monitoring, and/or tissue(s) and/or cell-type(s)-of-originassignment based on signal from these other tissues rather than thecancer cells per se.

In another aspect of the invention, the methods described herein areused for detection, monitoring and tissue(s) and/orcell-type(s)-of-origin assessment of tissue damage from analysis ofcfDNA in bodily fluids. It is to be expected that many pathologicalprocesses will result in a portion of cfDNA in bodily fluids such ascirculating plasma deriving from damaged tissues. The methods describedhere can potentially be used to detect and quantify cfDNA derived fromtissue damage, including identifying the relevant tissues and/orcell-types of origin. This may enable diagnosis and/or monitoring ofpathological processes including myocardial infarction (acute damage ofheart tissue), autoimmune disease (chronic damage of diverse tissues),and many others involving either acute or chronic tissue damage.

In another aspect of the invention, the methods described herein areused for estimating the fetal fraction of cfDNA in pregnancy and/orenhancing detection of chromosomal or other genetic abnormalities.Relatively shallow sequencing of the maternal plasma-borne DNAfragments, coupled with nucleosome maps described above, may allow acost-effective and rapid estimation of fetal fraction in both male andfemale fetus pregnancies. Furthermore, by enabling non-uniformprobabilities to be assigned to individual sequencing reads with respectto their likelihood of having originated from the maternal or fetalgenome, these methods may also enhance the performance of tests directedat detecting chromosomal aberrations (e.g. trisomies) through analysisof cfDNA in maternal bodily fluids.

In another aspect of the invention, the methods described herein areused for quantifying the contribution of a transplant (autologous orallograft) to cfDNA - Current methods for early and noninvasivedetection of acute allograft rejection involve sequencing plasma-borneDNA and identifying increased concentrations of fragments derived fromthe donor genome. This approach relies on relatively deep sequencing ofthis pool of fragments to detect, for example, 5-10% donor fractions. Anapproach based instead on nucleosome maps of the donated organ mayenable similar estimates with shallower sequencing, or more sensitiveestimates with an equivalent amount of sequencing. Analogous to cancer,it is also possible that cell types other than the transplant itselfcontribute to cfDNA composition during transplant rejection. To theextent that contributions from such other tissues to cfDNA areconsistent between patients during transplant rejection, the methodsdescribed above may enable monitoring of transplant rejection based onsignal from these other tissues rather than the transplant donor cellsper se.

Additional Embodiments of the Present Disclosure

The present disclosure also provides methods of diagnosing a disease ordisorder using nucleosome reference map(s) generated from subjectshaving a known disease or disorder. In some such embodiments, the methodcomprises: (1) generating a reference set of nucleosome maps, whereineach nucleosome map is derived from either cfDNA from bodily fluids ofindividual(s) with defined clinical conditions (e.g. normal, pregnancy,cancer type A, cancer type B, etc.) and/or DNA derived from digestion ofchromatin of specific tissues and/or cell types; (2) predicting theclinical condition and/or tissue/cell-type-of-origin composition ofcfDNA from bodily fluids of individual(s) by comparing a nucleosome mapderived from their cfDNA to the reference set of nucleosome maps.

STEP 1: Generating a reference set of nucleosome maps, and aggregatingor summarizing signal from nucleosome positioning.

A preferred method for generating a nucleosome map includes DNApurification, library construction (by adaptor ligation and possibly PCRamplification) and massively parallel sequencing of cfDNA from a bodilyfluid. An alternative source for nucleosome maps, which are useful inthe context of this invention as reference points or for identifyingprincipal components of variation, is DNA derived from digestion ofchromatin with micrococcal nuclease (MNase), DNase treatment, ATAC-Seqor other related methods wherein information about nucleosomepositioning is captured in distributions (a), (b) or (c). Descriptionsof these distributions (a), (b) and (c) are provided above in [0078] andare shown graphically in FIG. 1 .

In principle, very deep sequencing of such libraries can be used toquantify nucleosome occupancy in the aggregate cell types contributingto cfDNA at specific coordinates in the genome, but this is veryexpensive today. However, the signal associated with nucleosomeoccupancy patterns can be summarized or aggregated across continuous ordiscontinuous regions of the genome. For example, in Examples 1 and 2provided herein, the distribution of sites in the reference human genometo which sequencing read start sites map, i.e. distribution (a), issubjected to Fourier transformation in 10 kilobase-pair (kbp) contiguouswindows, followed by quantitation of intensities for frequency rangesthat are associated with nucleosome occupancy. This effectivelysummarizes the extent to which nucleosomes exhibit structuredpositioning within each 10 kbp window. In Example 3 provided herein, wequantify the distribution of sites in the reference human genome towhich sequencing read start sites map, i.e. distribution (a), in theimmediate vicinity of transcription factor binding sites (TFBS) ofspecific transcription factor (TF), which are often immediately flankedby nucleosomes when the TFBS is bound by the TF. This effectivelysummarizes nucleosome positioning as a consequence of TF activity in thecell type(s) contributing to cfDNA. Importantly, there are many relatedways in which nucleosome occupancy signals can be meaningfullysummarized. These include aggregating signal from distributions (a),(b), and/or (c) around other genomic landmarks such as DNaselhypersensitive sites, transcription start sites, topological domains,other epigenetic marks or subsets of all such sites defined bycorrelated behavior in other datasets (e.g. gene expression, etc.). Assequencing costs continue to fall, it will also be possible to directlyuse maps of nucleosome occupancy, including those generated from cfDNAsamples associated with a known disease, as reference maps, i.e. withoutaggregating signal, for the purposes of comparison to an unknown cfDNAsample. In some embodiments, this biological sample from which thereference map of nucleosome occupancy is generated is collected from ananimal to which human cells or tissues have been xenografted. Theadvantage of this is that sequenced cfDNA fragments mapping to the humangenome will exclusively derive from the xenografted cells or tissues, asopposed to representing a mixture of cfDNA derived from thecells/tissues of interest along with hematopoietic lineages.

STEP 2: Predicting pathology(s), clinical condition(s) and/ortissue/cell-types -of-origin composition on the basis of comparing thecfDNA-derived nucleosome map of one or more new individuals/samples tothe reference set of nucleosome maps either directly or aftermathematical transformation of each map.

Once one has generated a reference set of nucleosome maps, there are avariety of statistical signal processing methods for comparingadditional nucleosome map(s) to the reference set. In Examples 1 & 2, wefirst summarize long-range nucleosome ordering within 10 kbp windowsalong the genome in a diverse set of samples, and then perform principalcomponents analysis (PCA) to cluster samples (Example 1) or to estimatemixture proportions (Example 2). Although we know the clinical conditionof all cfDNA samples and the tissue/cell-type-of-origin of all cell linesamples used in these Examples, any one of the samples could inprinciple have been the “unknown”, and its behavior in the PCA analysisused to predict the presence/absence of a clinical condition or itstissue/cell-type-of-origin based on its behavior in the PCA analysisrelative to all other nucleosome maps.

The unknown sample does not necessarily need to be precisely matched to1+ members of the reference set in a 1:1 manner. Rather, itssimilarities to each can be quantified (Example 1), or its nucleosomemap can be modeled as a non-uniform mixture of 2+ samples from thereference set (Example 2).

The tissue/cell-type-of-origin composition of cfDNA in each sample neednot be predicted or ultimately known for the method of the presentinvention to be successful. Rather, the method described herein relieson the consistency of tissue/cell-type-of-origin composition of cfDNA inthe context of a particular pathology or clinical condition. However, bysurveying the nucleosome maps of a large number of tissues and/or celltypes directly by analysis of DNA derived from digestion of chromatinand adding these to the nucleosome map, it would be possible to estimatethe tissue(s) and/or cell-type(s) contributing to an unknowncfDNA-derived sample.

In any embodiment described herein, the method may further comprisegenerating a report comprising a statement identifying the disease ordisorder. In some embodiments, the report may further comprise a list ofthe determined tissues and/or cell types giving rise to the isolatedcfDNA. In some embodiments, the report further comprises a list ofdiseases and/or disorders which are unlikely to be associated with thesubject. The report may optionally further include any other informationabout the sample and/or the subject, the type of biological sample, thedate the biological sample was obtained from the subject, the date thecfDNA isolation step was performed and/or tissue(s) and/or cell type(s)which likely did not give rise to any cfDNA isolated from the biologicalsample.

In some embodiments, the report further includes a recommended treatmentprotocol including, for example and without limitation, a suggestion toobtain an additional diagnostic test from the subject, a suggestion tobegin a therapeutic regimen, a suggestion to modify an existingtherapeutic regimen with the subject, and/or a suggestion to suspend orstop an existing therapeutic regiment.

EXAMPLES EXAMPLE 1. Principal Components Analysis of Cell Free DNANucleosome Maps

The distribution of read start positions in sequencing data derived fromcfDNA extractions and MNase digestion experiments were examined toassess the presence of signals related to nucleosome positioning. Forthis purpose, a pooled cfDNA sample (human plasma containingcontributions from an unknown number of healthy individuals;bulk.cfDNA), a cfDNA sample from single healthy male control individual(MC2.cfDNA), four cfDNA samples from patients with intracranial tumors(tumor.2349, tumor.2350, tumor.2351, tumor.2353), six MNase digestionexperiments from five different human cell lines (Hap1.MNase,HeLa.MNase, HEK.MNase, NA12878.MNase, HeLaS3, MCF.7) and seven cfDNAsamples from different pregnant female individuals (gm1matplas,gm2matplas, im1matplas, fgs002, fgs003, fgs004, fgs005) were analyzedand contrasted with regular shotgun sequencing data set of DNA extractedfrom a female lymphoblastoid cell line (NA12878). A subset of the pooledcfDNA sample (26%, bulk.cfDNA_part) and of the single healthy malecontrol individual (18%, MC2.cfDNA_part) were also included, as separatesamples, to explore the effect of sequencing depth.

Read start coordinates were extracted and periodograms were createdusing Fast Fourier Transformation (FFT) as described in the Methodssection. This analysis determines how much of the non-uniformity in thedistribution of read start sites can be explained by signals of specificfrequencies/periodicities. We focused on a range of 120-250 bp, whichcomprises the length range of DNA wrapped around a single nucleosome(147 bp) as well as additional sequence of the nucleosome linkersequence (10-80 bp). FIG. 3 shows the average intensities for eachfrequency across all blocks of human chromosome 1 and human chromosome22. It can be seen that MNase digestion experiments as well cfDNAsamples show clear peaks below 200 bp periodicity. Such a peak is notobserved in the human shotgun data. These analyses are consistent with amajor effect of nucleosome positioning on the distribution of fragmentboundaries in cfDNA.

Variation in the exact peak frequency between samples was also observed.This is possibly a consequence of different distributions of linkersequence lengths in each cell type. That the peak derives from patternsof nucleosome bound DNA plus linker sequence is supported by theobservations that the flanks around the peaks are not symmetrical andthat the intensities for frequencies higher than the peak compared tofrequencies lower than the peak are lower. This suggests that plotssimilar to those presented in FIG. 3 can be used to perform qualitycontrol of cfDNA and MNase sequencing data. Random fragmentation orcontamination of cfDNA and MNase with regular (shotgun) DNA will causedilution or, in extreme cases, total absence of these characteristicintensity patterns in periodograms.

In the following, data were analyzed based on measured intensities at aperiodicity of 196 bp as well as all intensities determined for thefrequency range of 181 bp to 202 bp. A wider frequency range was chosenin order to provide higher resolution because a wider range of linkerlengths are being captured. These intensities were chosen as the focuspurely for computational reasons here; different frequency ranges may beused in related embodiments. FIGS. 4 and 5 , explore visualizations ofthe periodogram intensities at 196 bp across contiguous, non-overlapping10 kbp blocks tiling the full length of human autosomes (see Methods fordetails). FIG. 4 presents a Principal Component Analysis (PCA) of thedata and the projections across the first three components. Principalcomponent 1 (PC1) (28.1% of variance) captures the differences inintensity strength seen in FIG. 3 and thereby separates MNase and cfDNAsamples from genomic shotgun data. In contrast, PC2 (9.7% of variance)captures the differences between MNase and cfDNA samples. PC3 (6.4%variance) captures differences between individual samples. FIG. 5 showsthe hierarchical clustering dendogram of this data based on Euclideandistances of the intensity vectors. We note that the two HeLa S3experiments tightly cluster in the PCA and dendogram, even though datawas generated in different labs and following different experimentalprotocols. “Normal” cfDNA samples, tumor cfDNA samples and groups ofcell line MNase samples also clustered. Specifically, the three tumorsamples originating from the same tumor type (glioblastoma multiforme)appear to cluster, separately from tumor.2351 sample which originatesfrom a different tumor type (see Table 1). The GM1 and IM1 samplescluster separately from the other cfDNA samples obtained from pregnantwomen. This coincides with higher intensities observed for frequenciesbelow the peak in these samples (i.e., a more pronounced left shoulderin FIG. 3 ). This might indicate subtle differences in the preparationof the cfDNA between the two sets of samples, or biological differenceswhich were not controlled for (e.g., gestational age).

FIGS. 6 and 7 show the results of equivalent analyses but based on thefrequency range of 181 bp to 202 bp. Comparing these plots, the resultsare largely stable to a wider frequency range; however additionalfrequencies may improve sensitivity in more fine-scaled analyses. Tofurther explore cell-type origin specific patterns, the cfDNA and MNasedata sets were analyzed separately using PCA of intensities for thisfrequency range. In the following set of analyses, the five cfDNAsamples from pregnant women, which show the pronounced left shoulder inFIG. 3 , were excluded. FIG. 8 shows the first 7 principal components ofthe cfDNA data and FIG. 9 all six principal components for the six MNasedata sets. While there is a clustering of related samples, there is alsoconsiderable variation (biological and technical variation) to separateeach sample from the rest. For example, an effect of sequencing depthwas observable, as can be seen from the separation of bulk.cfDNA andbulk.cfDNA_part as well as MC2.cfDNA and MC2.cfDNA_part. Read samplingmay be used to correct for this technical confounder.

Some key observations of this example include:

-   1) Read start coordinates in cfDNA sequencing data capture a strong    signal of nucleosome positioning.-   2) Differences in the signal of nucleosome positioning, aggregated    across subsets of the genome such as contiguous 10 kbp windows,    correlate with sample origin.

EXAMPLE 2 - Mixture Proportion Estimation of Nucleosome Maps

In Example 1, basic clustering of samples that were generated ordownloaded from public databases was studied. The analyses showed thatread start coordinates in these data sets capture a strong signal ofnucleosome positioning (across a range of sequencing depths obtainedfrom 20 million sequences to more than a 1,000 million sequences) andthat sample origin correlates with this signal. For the goals of thismethod, it would also be useful to be able to identify mixtures of knowncell types and to some extent quantify the contributions of each celltype from this signal. For this purpose, this example explored syntheticmixtures (i.e., based on sequence reads) of two samples. We mixedsequencing reads in ratios of 5:95, 10:90, 15:85, 20:80, 30:70, 40:60,50:50, 60:40, 30:70, 80:20, 90:10 and 95:5 for two MNase data sets(MCF.7 and NA12878.MNase) and two cfDNA data sets (tumor.2349 andbulk.cfDNA). The synthetic MNase mixture datasets were drawn from twosets of 196.9 million aligned reads (each from one of the originalsamples) and the synthetic cfDNA mixture datasets were drawn from twosets of 181.1 million aligned reads (each from one of the originalsamples).

FIG. 10 shows the average intensities for chromosome 11, equivalent toFIG. 3 but for these synthetic mixtures. It can be seen from FIG. 10 howthe different sample contributions cause shifts in the global frequencyintensity patterns. This signal can be exploited to infer the syntheticmixture proportions. FIG. 11 shows the first two principal componentsfor the MNase data set mixtures and FIG. 12 shows the first twoprincipal components for the cfDNA data set mixtures. In both cases, thefirst PC directly captures the composition of the mixed data set. It istherefore directly conceivable how mixture proportions for two andpossibly more cell types could be estimated from transformation of thefrequency intensity data given the appropriate reference sets and usingfor example regression models. FIG. 13 shows the dendogram of both datasets, confirming the overall similarities of mixture samples derivingfrom similar sample proportions as well as the separation of the cfDNAand MNase samples.

One of the key observations of this example is that the mixtureproportions of various sample types (cfDNA or cell/tissue types) to anunknown sample can be estimated by modeling of nucleosome occupancypatterns.

EXAMPLE 3: Measuring Nucleosome Occupancy Relative to TranscriptionFactor Binding Sites with cfDNA Sequencing Data

While previous examples demonstrate that signals of nucleosomepositioning can be obtained by partitioning the genome into contiguous,non-overlapping 10 kbp windows, orthogonal methods can also be used togenerate cleavage accessibility maps and may be less prone to artifactsbased on window size and boundaries. One such method, explored in somedetail in this Example, is the inference of nucleosome positioningthrough observed periodicity of read-starts around transcription factor(TF) binding sites.

It is well established that local nucleosome positioning is influencedby nearby TF occupancy. The effect on local remodeling of chromatin, andthus on the stable positioning of nearby nucleosomes, is not uniformacross the set of TFs; occupancy of a given TF may have local effects onnucleosome positioning that are preferentially 5′ or 3′ of the bindingsite and stretch for greater or lesser genomic distance in specific celltypes. Furthermore, and importantly for the purposes of this disclosure,the set of TF binding sites occupied in vivo in a particular cell variesbetween tissues and cell types, such that if one were able to identifyTF binding site occupancy maps for tissues or cell types of interest,and repeated this process for one or more TFs, one could identifycomponents of the mixture of cell types and tissues contributing to apopulation of cfDNA by identifying enrichment or depletion of one ormore cell type- or tissue-specific TF binding site occupancy profiles.

To demonstrate this idea, read-starts in the neighborhood of TF bindingsites were used to visually confirm cleavage biases reflective ofpreferential local nucleosome positioning. ChIP-seq transcription factor(TF) peaks were obtained from the Encyclopedia of DNA Elements(“ENCODE”) project (National Human Genome Research Institute, NationalInstitutes of Health, Bethesda, MD). Because the genomic intervals ofthese peaks are broad (200 to 400 bp on average), the active bindingsites within these intervals were discerned by informatically scanningthe genome for respective binding motifs with a conservative p-valuecutoff (1x10⁻⁵, see Methods for details). The intersection of these twoindependently derived sets of predicted TF binding sites were thencarried forward into downstream analysis.

The number of read-starts at each position within 500 bp of eachcandidate TF binding site was calculated in samples with at least 100million sequences. Within each sample, all read-starts were summed ateach position, yielding a total of 1,014 to 1,019 positions per sampleper TF, depending on the length of the TF recognition sequence.

FIG. 14 shows the distribution of read-starts around 24,666 CTCF bindingsites in the human genome in a variety of different samples, centeredaround the binding site itself. CTCF is an insulator binding protein andplays a major role in transcriptional repression. Previous studiessuggest that CTCF binding sites anchor local nucleosome positioning suchthat at least 20 nucleosomes are symmetrically and regularly spacedaround a given binding site, with an approximate period of 185 bp. Onestriking feature common to nearly all of the samples in FIG. 14 is theclear periodicity of nucleosome positioning both upstream and downstreamof the binding site, suggesting that the local and largely symmetricaleffects of CTCF binding in vivo are recapitulated in a variety of cfDNAand MNase-digested samples. Intriguingly, the periodicity of theupstream and downstream peaks is not uniform across the set of samples;the MNase-digested samples display slightly wider spacing of the peaksrelative to the binding site, suggesting the utility of not only theintensity of the peaks, but also their period.

FIG. 15 shows the distribution of read-starts around 5,644 c-Jun bindingsites. While the familiar periodicity is again visually identifiable forseveral samples in this figure, the effect is not uniform. Of note,three of the MNase-digested samples (Hap1.MNase, HEK.MNase, andNA12878.MNase) have much flatter distributions, which may indicate thatc-Jun binding sites are not heavily occupied in these cells, or that theeffect of c-Jun binding on local chromatin remodeling is less pronouncedin these cell types. Regardless of the underlying mechanism, theobservation that bias in the local neighborhood of read-starts variesfrom TF to TF and between sample types reinforces the potential role forread start-based inference of nucleosome occupancy for correlating ordeconvoluting tissue-of-origin composition in cfDNA samples.

FIG. 16 shows the distribution of read-starts around 4,417 NF-YB bindingsites. The start site distributions in the neighborhood of these TFbinding sites demonstrate a departure from symmetry: here, thedownstream effects (to the right within each plot) appear to be strongerthan the upstream effects, as evidenced by the slight upward trajectoryin the cfDNA samples. Also of note is the difference between theMNase-digested samples and the cfDNA samples: the former show, onaverage, a flatter profile in which peaks are difficult to discern,whereas the latter have both more clearly discernable periodicity andmore identifiable peaks.

Methods for Examples 1-3 Clinical and Control Samples

Whole blood was drawn from pregnant women fgs002, fgs003, fgs004, andfgs005 during routine third-trimester prenatal care and stored brieflyin Vacutainer tubes containing EDTA (BD). Whole blood from pregnantwomen IM1, GM1, and GM2 was obtained at 18, 13, and 10 weeks gestation,respectively, and stored briefly in Vacutainer tubes containing EDTA(BD). Whole blood from glioma patients 2349, 2350, 2351, and 2353 wascollected as part of brain surgical procedures and stored for less thanthree hours in Vacutainer tubes containing EDTA (BD). Whole blood fromMale Control 2 (MC2), a healthy adult male, was collected in Vacutainertubes containing EDTA (BD). Four to ten ml of blood was available foreach individual. Plasma was separated from whole blood by centrifugationat 1,000 x g for 10 minutes at 4° C., after which the supernatant wascollected and centrifuged again at 2,000 x g for 15 minutes at 4° C.Purified plasma was stored in 1 ml aliquots at -80° C. until use.

Bulk human plasma, containing contributions from an unknown number ofhealthy individuals, was obtained from STEMCELL Technologies (Vancouver,British Columbia, Canada) and stored in 2 ml aliquots at -80° C. untiluse.

Processing of Plasma Samples

Frozen plasma aliquots were thawed on the bench-top immediately beforeuse. Circulating cfDNA was purified from 2 ml of each plasma sample withthe QiaAMP Circulating Nucleic Acids kit (Qiagen, Venlo, Netherlands) asper the manufacturer’s protocol. DNA was quantified with a Qubitfluorometer (Invitrogen, Carlsbad, California) and a custom qPCR assaytargeting a human Alu sequence.

MNase Digestions

Approximately 50 million cells of each line (GM12878, HeLa S3, HEK,Hap1) were grown using standard methods. Growth media was aspirated andcells were washed with PBS. Cells were trypsinized and neutralized with2x volume of CSS media, then pelleted in conical tubes by centrifugationfor at 1,300 rpm for 5 minutes at 4° C. Cell pellets were resuspended in12 ml ice-cold PBS with 1x protease inhibitor cocktail added, counted,and then pelleted by centrifugation for at 1,300 rpm for 5 minutes at 4°C. Cell pellets were resuspended in RSB buffer (10 mM Tris-HCl, 10 mMNaCl, 3 mM MgCl₂, 0.5 mM spermidine, 0.02% NP-40, 1X protease inhibitorcocktail) to a concentration of 3 million cells per ml and incubated onice for 10 minutes with gentle inversion. Nuclei were pelleted bycentrifugation at 1,300 rpm for five minutes at 4° C. Pelleted nucleiwere resuspended in NSB buffer (25% glycerol, 5 mM MgAc₂, 5 mM HEPES,0.08 mM EDTA, 0.5 mM spermidine, 1 mM DTT, 1x protease inhibitorcocktail) to a final concentration of 15 M per ml. Nuclei were againpelleted by centrifugation at 1,300 rpm for 5 minutes at 4° C., andresuspended in MN buffer (500 mM Tris-HCl, 10 mM NaCl, 3 mM MgCl₂, 1 mMCaCl, 1x protease inhibitor cocktail) to a final concentration of 30 Mper ml. Nuclei were split into 200 µl aliquots and digested with 4 U ofmicrococcal nuclease (Worthington Biochemical Corp., Lakewood, NJ, USA)for five minutes at 37° C. The reaction was quenched on ice with theaddition of 85 µl of MNSTOP buffer (500 mM NaCl, 50 mM EDTA, 0.07%NP-40, 1x protease inhibitor), followed by a 90 minute incubation at 4°C. with gentle inversion. DNA was purified usingphenol:chloroform:isoamyl alcohol extraction. Mononucleosomal fragmentswere size selected with 2% agarose gel electrophoresis using standardmethods and quantified with a Nanodrop spectrophotometer (Thermo FisherScientific Inc., Waltham, MA, USA).

Preparation of Sequencing Libraries

Barcoded sequencing libraries for all samples were prepared with theThruPLEX-FD or ThruPLEX DNA-seq 48D kits (Rubicon Genomics, Ann Arbor,Michigan), comprising a proprietary series of end-repair, ligation, andamplification reactions. Between 3.0 and 10.0 ng of DNA were used asinput for all clinical sample libraries. Two bulk plasma cfDNA librarieswere constructed with 30 ng of input to each library; each library wasseparately barcoded. Two libraries from MC2 were constructed with 2 ngof input to each library; each library was separately barcoded.Libraries for each of the MNase-digested cell lines were constructedwith 20 ng of size-selected input DNA. Library amplification for allsamples was monitored by real-time PCR to avoid over-amplification.

Sequencing

All libraries were sequenced on HiSeq 2000 instruments (Illumina, Inc.,San Diego, CA, USA) using paired-end 101 bp reads with an index read of9 bp. One lane of sequencing was performed for pooled samples fgs002,fgs003, fgs004, and fgs005, yielding a total of approximately 4.5x10⁷read-pairs per sample. Samples IM1, GM1, and GM2 were sequenced acrossseveral lanes to generate 1.2x10⁹, 8.4x10⁸, and 7.6x10⁷ read-pairs,respectively. One lane of sequencing was performed for each of samples2349, 2350, 2351, and 2353, yielding approximately 2.0x10⁸ read-pairsper sample. One lane of sequencing was performed for each of the fourcell line MNase-digested libraries, yielding approximately 2.0x10⁸read-pairs per library. Four lanes of sequencing were performed for oneof the two replicate MC2 libraries and three lanes for one of the tworeplicate bulk plasma libraries, yielding a total of 10.6x10⁹ and7.8x10⁸ read-pairs per library, respectively.

Processing of cfDNA Sequencing Data

DNA insert sizes for both cfDNA and MNase libraries tend be short(majority of data between 80 bp and 240 bp); adapter sequence at theread ends of some molecules were therefore expected. Adapter sequencesstarting at read ends were trimmed, and forward and reverse read ofpaired end (“PE”) data for short original molecules were collapsed intosingle reads (“SRs”); PE reads that overlap with at least 11 bp readswere collapsed to SRs. The SRs shorter than 30 bp or showing more than 5bases with a quality score below 10 were discarded. The remaining PE andSR data were aligned to the human reference genome (GRCh37, 1000Grelease v2) using fast alignment tools (BWA-ALN or BWA-MEM). Theresulting SAM (Sequence Alignment/Map) format was converted to sortedBAM (Binary Sequence Alignment/Map format) using SAMtools.

Additional Publically Available Data

Publically available PE data of Hela-S3 MNase (accessions SRR633612,SRR633613) and MCF-7 MNase experiments (accessions SRR999659-SRR999662)were downloaded and processed as described above.

Publicly available genomic shotgun sequencing data of the CEPH pedigree146 individual NA12878 generated by Illumina Cambridge Ltd. (Essex, UK)was obtained from the European Nucleotide Archive (ENA, accessionsERR174324-ERR174329). This data was PE sequenced with 2x101 bp reads onthe Illumina HiSeq platform and the libraries were selected for longerinsert sizes prior to sequencing. Thus, adapter sequence at the readends were not expected; this data was therefore directly aligned usingBWA-MEM.

Extracting Read End Information

PE data provides information about the two physical ends of DNAmolecules used in sequencing library preparation. This information wasextracted using the SAMtools application programming interface (API)from BAM files. Both outer alignment coordinates of PE data for whichboth reads aligned to the same chromosome and where reads have oppositeorientations were used. For non-trimmed SR data, only one read endprovides information about the physical end of the original DNAmolecule. If a read was aligned to the plus strand of the referencegenome, the left-most coordinate was used. If a read was aligned to thereverse strand, its right-most coordinate was used instead. In caseswhere PE data was converted to single read data by adapter trimming,both end coordinates were considered. Both end coordinates were alsoconsidered if at least five adapter bases were trimmed from a SRsequencing experiment.

For all autosomes in the human reference sequence (chromosomes 1 to 22),the number of read ends and the coverage at all positions were extractedin windows of 10,000 bases (blocks). If there were no reads aligning ina block, the block was considered empty for that specific sample.

Smooth Periodograms

The ratio of read-starts and coverage was calculated for each non-emptyblock of each sample. If the coverage was 0, the ratio was set to 0.These ratios were used to calculate a periodogram of each block usingFast Fourier Transform (FFT, spec.pgram in the R statistical programmingenvironment) with frequencies between 1/500 bases and 1/100 bases.Optionally, parameters to smooth (3 bp Daniell smoother; moving averagegiving half weight to the end values) and detrend the data (e.g.,subtract the mean of the series and remove a linear trend) were used.Intensities for the frequency range 120-250 bp for each block weresaved.

Average Chromosome Intensities

For a set of samples, blocks that were non-empty across all samples wereidentified. The intensities for a specific frequency were averagedacross all blocks of each sample for each autosome.

Principal Component Analysis and Dendograms

Blocks that were non-empty across samples were collected. Principalcomponent analysis (PCA; prcomp in the R statistical programmingenvironment) was used to reduce the dimensionality of the data and toplot it in two-dimensional space. PCA identifies the dimension thatcaptures most variation of the data and constructs orthogonaldimensions, explaining decreasing amounts of variation in the data.

Pair-wise Euclidean distances between sample intensities were calculatedand visualized as dendograms (stats library in the R statisticalprogramming environment).

Transcription Factor Binding Site Predictions

Putative transcription factor binding sites, obtained through analysisof ChIP-seq data generated across a number of cell types, was obtainedfrom the ENCODE project.

An independent set of candidate transcription factor binding sites wasobtained by scanning the human reference genome (GRCh37, 1000G releasev2) with the program fimo from the MEME software package (version4.10.0_1). Scans were performed using positional weight matricesobtained from the JASPAR_CORE_2014_vertebrates database, using options“--verbosity 1 --thresh 1e-5”. Transcription factor motif identifiersused were MA0139.1, MA0502.1, and MA0489.1.

Chromosomal coordinates from both sets of predicted sites wereintersected with bedtools v2.17.0. To preserve any asymmetry in theplots, only predicted binding sites on the “+” strand were used.Read-starts were tallied for each sample if they fell within 500 bp ofeither end of the predicted binding site, and summed within samples byposition across all such sites. Only samples with at least 100 milliontotal reads were used for this analysis.

EXAMPLE 4: Determining Normal/Healthy Tissue(s)-of-Origin From cfDNA

To evaluate whether fragmentation patterns observed in a singleindividual’s cfDNA might contain evidence of the genomic organization ofthe cells giving rise to these fragments—and thus, of thetissue(s)-of-origin of the population of cfDNA molecules -even whenthere are no genotypic differences between contributing cell types,cfDNA was deeply sequenced to better understand the processes that giverise to it. The resulting data was used to build a genome-wide map ofnucleosome occupancy that built on previous work by others, but issubstantially more comprehensive. By optimizing library preparationprotocols to recover short fragments, it was discovered that the in vivooccupancies of transcription factors (TFs) such as CTCF are alsodirectly footprinted by cfDNA. Finally, it was discovered thatnucleosome spacing in regulatory elements and gene bodies, as revealedby cfDNA sequencing in healthy individuals, correlates most stronglywith DNase hypersensitivity and gene expression in lymphoid and myeloidcell lines.

cfDNA Fragments Correspond to Chromatosomes and Contain Substantial DNADamage

Conventional sequencing libraries were prepared by end-repair andadaptor ligation to cfDNA fragments purified from plasma pooled from anunknown number of healthy individuals (“BH01”) or plasma from a singleindividual (“IH01”) (FIG. 17 ; Table 1):

TABLE 1 Sequencing Statistics for Plasma Samples Sample name Librarytype Reads Fragments sequenced Aligned Aligned Q30 Coverage Est. %duplicates 35-80 bp 120-180 bp BH01 DSP 2x101 1489569204 97.20% 88.85%96.32 6.00% 0.65% 57.64% IH01 DSP 2x101 1572050374 98.58% 90.60% 104.9221.00% 0.77% 47.83% IH02 SSP 2x50, 43/42 779794090 93.19% 75.27% 30.0820.05% 21.83% 44.00% CH01 3841413668 96.95% 86.81% 231.32 14.99% 5.00%50.85% SSP, single-stranded library preparation protocol. DSP,double-stranded library preparation protocol.

For each sample, sequencing-related statistics, including the totalnumber of fragments sequenced, read lengths, the percentage of suchfragments aligning to the reference with and without a mapping qualitythreshold, mean coverage, duplication rate, and the proportion ofsequenced fragments in two length bins, were tabulated. Fragment lengthwas inferred from alignment of paired-end reads. Due to the short readlengths, coverage was calculated by assuming the entire fragment hadbeen read. The estimated number of duplicate fragments was based onfragment endpoints, which may overestimate the true duplication rate inthe presence of highly stereotyped cleavage. SSP, single-strandedlibrary preparation protocol. DSP, double-stranded library preparationprotocol.

Libraries BH01 and IH01 were sequenced to 96- and 105-fold coverage,respectively (1.5G and 1.6G fragments). The fragment lengthdistributions, inferred from alignment of paired-end reads, have adominant peak at ~167 bp (coincident with the length of DNA associatedwith a chromatosome), and ~10.4 bp periodicity in the 100-160 bp lengthrange (FIG. 18 ). These distributions are consistent with a model inwhich cfDNA fragments are preferentially protected from nucleasecleavage both pre- and post-cell death by association with proteins—inthis case, by the nucleosome core particle and linker histone—but wheresome degree of additional nicking or cleavage occurs in relation to thehelical pitch of nucleosome-bound DNA. Further supporting this model isthe dinucleotide composition of these 167 bp fragments, whichrecapitulate key features of earlier studies of MNase-derived,nucleosome-associated fragments (e.g. bias against A/T dinucleotides atthe dyad) and support the notion that the nucleosome core particle issymmetrically positioned with respect to the chromatosome (FIG. 19 ).

A prediction of this model of cfDNA ontology is widespread DNA damage,e.g. single-strand nicks as well as 5′ and 3′ overhangs. Duringconventional library preparation, nicked strands are not amplified,overhangs are blunted by end-repair, and short double stranded DNA(“dsDNA”) molecules, which may represent a substantial proportion oftotal cfDNA, may simply be poorly recovered. To address this, asingle-stranded sequencing library from plasma-borne cfDNA derived froman additional healthy individual (‘IH02’) was prepared using a protocoladapted from studies of ancient DNA by Gansauge, et al., wherewidespread DNA damage and nuclease cleavage around nucleosomes have beenreported. Briefly, cfDNA was denatured and a biotin-conjugated,single-stranded adaptor was ligated to the resulting fragments. Theligated fragments were then subjected to second-strand synthesis,end-repair and ligation of a second adaptor while the fragments wereimmobilized to streptavidin beads. Finally, minimal PCR amplificationwas performed to enrich for adaptor-bearing molecules while alsoappending a sample index (FIG. 20 ; Table 2).

TABLE 2 Synthetic oligos used in preparation of single strandedsequencing libraries Oligo Name SEQ ID NO Sequence (5′-3′) Notes CL9 1GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT HPLC purification Adapter2.1 2CGACGCTCTTCCGATC/ddT/ HPLC purification Adapter2.2 3/5Phos/AGATCGGAAGAGCGTCGTGTAGGGAAAGAG*T*G*T *A HPLC purification CL78 4/5Phos/AGATCGGAAG/iSpC3/iSpC3/iSpC3/iSpC3/iSpC3/iSpC3/iSpC3/iSpC3/iSpC3/iSpC3/3BioTEG/ Dual HPLC purification

For IH02, the resulting library was sequenced to 30-fold coverage (779 Mfragments). The fragment length distribution again exhibited a dominantpeak at ~167 bp corresponding to the chromatosome, but was considerablyenriched for shorter fragments relative to conventional librarypreparation (FIGS. 21, 22, 23A-B, 24A-B). Although all libraries exhibit~10.4 bp periodicity, the fragment sizes are offset by 3 bp for the twomethods, consistent with damaged or non-flush input molecules whose trueendpoints are more faithfully represented in single-stranded libraries.

A Genome-Wide Map of in Vivo Nucleosome Protection Based on Deep cfDNASequencing

To assess whether the predominant local positions of nucleosomes acrossthe human genome in tissue(s) contributing to cfDNA could be inferred bycomparing the distribution of aligned fragment endpoints, or amathematical transformation thereof, to one or more reference maps, aWindowed Protection Score (“WPS”) was developed. Specifically, it wasexpected that cfDNA fragment endpoints should cluster adjacent tonucleosome boundaries, while also being depleted on the nucleosomeitself. To quantify this, the WPS was developed, which represents thenumber of DNA fragments completely spanning a 120 bp window centered ata given genomic coordinate, minus the number of fragments with anendpoint within that same window (FIG. 25 ). As intended, the value ofthe WPS correlates with the locations of nucleosomes within stronglypositioned arrays, as mapped by other groups with in vitro methods orancient DNA (FIG. 26 ). At other sites, the WPS correlates with genomicfeatures such as DNase I hypersensitive (DHS) sites (e.g., consistentwith the repositioning of nucleosomes flanking a distal regulatoryelement) (FIG. 27 ).

A heuristic algorithm was applied to the genome-wide WPS of the BH01,IH01 and IH02 datasets to identify 12.6 M, 11.9 M, and 9.7 M localmaxima of nucleosome protection, respectively (FIGS. 25-31 ). In eachsample, the mode of the distribution of distances between adjacent peakswas 185 bp with low variance (FIG. 30 ), generally consistent withprevious analyses of the nucleosome repeat length in human or mousecells.

To determine whether the positions of peak calls were similar acrosssamples, the genomic distance for each peak in a sample to the nearestpeak in each of the other samples was calculated. High concordance wasobserved (FIG. 31 ; FIGS. 32A-C). The median (absolute) distance from aBH01 peak call to a nearest-neighbor IH01 peak call was 23 bp overall,but was less than 10 bp for the most highly scored peaks (FIGS. 33A-B).

Because biases introduced either by nuclease specificity or duringlibrary preparation might artifactually contribute to the signal ofnucleosome protection, fragment endpoints were also simulated, matchingfor the depth, size distribution and terminal dinucleotide frequenciesof each sample. Genome-wide WPS were then calculated, and 10.3 M, 10.2M, and 8.0 M were called local maxima by the same heuristic, forsimulated datasets matched to BH01, IH01 and IH02, respectively. Peaksfrom simulated datasets were associated with lower scores than peaksfrom real datasets (FIGS. 33A-B). Furthermore, the relativelyreproducible locations of peaks called from real datasets (FIG. 31 ;FIGS. 32A-C) did not align well with the locations of peaks called fromsimulated datasets (FIG. 31 ; FIGS. 34A-C).

To improve the precision and completeness of the genome-wide nucleosomemap, the cfDNA sequencing data from BH01, IH01, and IH02 were pooled andreanalyzed for a combined 231 fold-coverage (‘CH01’; 3.8B fragments;Table 1). The WPS was calculated and 12.9M peaks were called for thiscombined sample. This set of peak calls was associated with higherscores and approached saturation in terms of the number of peaks (FIGS.33A-B). Considering all peak-to-peak distances that were less than 500bp (FIG. 35 ), the CH01 peak set spans 2.53 gigabases (Gb) of the humanreference genome.

Nucleosomes are known to be well-positioned in relation to landmarks ofgene regulation, for example transcriptional start sites and exon-intronboundaries. Consistent with that understanding, similar positioning wasobserved in this data as well, in relation to landmarks oftranscription, translation and splicing (FIGS. 36-40 ). Building on pastobservations of correlations between nucleosome spacing withtranscriptional activity and chromatin marks, the median peak-to-peakspacing within 100 kilobase (kb) windows that had been assigned tocompartment A (enriched for open chromatin) or compartment B (enrichedfor closed chromatin) on the basis of long-range interactions (in situHi-C) in a lymphoblastoid cell line was examined. Nucleosomes incompartment A exhibited tighter spacing than nucleosomes in compartmentB (median 187 bp (A) vs. 190 bp (B)), with further differences betweencertain subcompartments (FIG. 41 ). Along the length of chromosomes, nogeneral pattern was seen, except that median nucleosome spacing droppedsharply in pericentromeric regions, driven by strong positioning acrossarrays of alpha satellites (171 bp monomer length; FIG. 42 ; FIG. 26 ).

Short cfDNA Fragments Directly Footprint CTCF and Other TranscriptionFactors

Previous studies of DNase I cleavage patterns identified two dominantclasses of fragments: longer fragments associated with cleavage betweennucleosomes, and shorter fragments associated with cleavage adjacent totranscription factor binding sites (TFBS). To assess whether invivo-derived cfDNA fragments also resulted from two classes ofsensitivity to nuclease cleavage, sequence reads (CH01) were partitionedon the basis of inferred fragment length, and the WPS was recalculatedusing long fragments (120-180 bp; 120 bp window; effectively the same asthe WPS described above for nucleosome calling) or short fragments(35-80 bp; 16 bp window) separately (FIGS. 26-27 ). To obtain a set ofwell-defined TFBSs enriched for actively bound sites in our data,clustered FIMO predictions were intersected with a unified set ofChlP-seq peaks from ENCODE (TfbsClusteredV3) for each TF.

The long fraction WPS supports strong organization of nucleosomes in thevicinity of CTCF binding sites (FIG. 43 ). However, a strong signal inthe short fraction WPS is also observed that is coincident with the CTCFbinding site itself (FIGS. 44-45 ). CTCF binding sites were stratifiedbased on a presumption that they are bound in vivo (all FIMO predictionsvs. the subset intersecting with ENCODE ChlP-seq vs. the further subsetintersecting with those that appear to be utilized across 19 celllines). Experimentally well -supported CTCF sites exhibit asubstantially broader spacing between the flanking -1 and +1 nucleosomesbased on the long fraction WPS, consistent with their repositioning uponCTCF binding (~190 bp → ~260 bp; FIGS. 45-48 ). Furthermore,experimentally well -supported CTCF sites exhibit a much stronger signalfor the short fraction WPS over the CTCF binding site itself (FIGS.49-52 ).

Similar analyses were performed for additional TFs for which both FIMOpredictions and ENCODE CHiP-seq data were available (FIGS. 53A-H). Formany of these TFs, such as ETS and MAFK (FIGS. 54-55 ), a short fractionfootprint was observed, accompanied by periodic signal in the longfraction WPS. This is consistent with strong positioning of nucleosomessurrounding bound TFBS. Overall, these data support the view that shortcfDNA fragments, which are recovered markedly better by thesingle-stranded protocol (FIG. 18 , FIG. 21 ), directly footprint the invivo occupancy of DNA-bound transcription factors, including CTCF andothers.

Nucleosome Spacing Patterns Inform cfDNA Tissues-of-Origin

To determine whether in vivo nucleosome protection, as measured throughcfDNA sequencing, could be used to infer the cell types contributing tocfDNA in healthy individuals, the peak-to-peak spacing of nucleosomecalls within DHS sites defined in 116 diverse biological samples wasexamined. Widened spacing was previously observed between the -1 and +1nucleosomes at regulatory elements (e.g., anecdotally at DHS sites (FIG.27 ) or globally at bound CTCF sites (FIG. 45 )). Similar to bound CTCFsites, substantially broader spacing was observed for nucleosome pairswithin a subset of DHS sites, plausibly corresponding to sites at whichthe nucleosomes are repositioned by intervening transcription factorbinding in the cell type(s) giving rise to cfDNA (~190 bp → ~260 bp;FIG. 56 ). Indeed, the proportion of widened nucleosome spacing (~260bp) varies considerably depending on which cell type’s DHS sites areused. However, all of the cell types for which this proportion ishighest are lymphoid or myeloid in origin (e.g., CD3_CB-DS17706, etc. inFIG. 56 ). This is consistent with hematopoietic cell death as thedominant source of cfDNA in healthy individuals.

Next the signal of nucleosome protection in the vicinity oftranscriptional start sites was re-examined (FIG. 36 ). When the signalwas stratified based on gene expression in a lymphoid lineage cell line,NB-4, strong differences in the locations or intensity of nucleosomeprotection in relation to the TSS were observed, in highly vs. lowlyexpressed genes (FIG. 57 ). Furthermore, the short fraction WPS exhibitsa clear footprint immediately upstream of the TSS whose intensity alsostrongly correlates with expression level (FIG. 58 ). This plausiblyreflects footprinting of the transcription preinitiation complex, orsome component thereof, at transcriptionally active genes.

These data demonstrate that cfDNA fragmentation patterns do indeedcontain signal that might be used to infer the tissue(s) or cell-type(s)giving rise to cfDNA.

However, a challenge is that relatively few reads in a genome-wide cfDNAlibrary directly overlap DHS sites and transcriptional start sites.

Nucleosome spacing varies between cell types, and as a function ofchromatin state and gene expression. In general, open chromatin andtranscription are associated with a shorter nucleosome repeat length,consistent with this Example’s analyses of compartment A vs. B (FIG. 41). This Example’s peak call data also exhibits a correlation betweennucleosome spacing across gene bodies and their expression levels, withtighter spacing associated with higher expression (FIG. 59 ; ρ = -0.17;n = 19,677 genes). The correlation is highest for the gene body itself,relative to adjacent regions (upstream 10 kb ρ = -0.08; downstream 10 kbρ = -0.01). If the analysis is limited to gene bodies that span at least60 nucleosome calls, tighter nucleosome spacing is even more stronglycorrelated with gene expression (p = -0.50; n = 12,344 genes).

One advantage of exploiting signals such as nucleosome spacing acrossgene bodies or other domains is that a much larger proportion of cfDNAfragments will be informative. Another potential advantage is thatmixtures of signals resulting from multiple cell types contributing tocfDNA might be detectable. To test this, a further mathematicaltransformation, fast Fourier transformation (FFT), was performed on thelong fragment WPS across the first 10 kb of gene bodies and on agene-by-gene basis. The intensity of the FFT signal correlated with geneexpression at specific frequency ranges, with a maximum at 177-180 bpfor positive correlation and a minimum at ~199 bp for negativecorrelation (FIG. 60 ). In performing this analysis against a dataset of76 expression datasets for human cell lines and primary tissues, thestrongest correlations were with hematopoietic lineages (FIG. 60 ). Forexample, the most highly ranked negative correlations with averageintensity in the 193-199 bp frequency range for each of three healthysamples (BH01, IH01, IH02) were all to lymphoid cell lines, myeloid celllines, or bone marrow tissue (FIG. 61 ; Table 3):

TABLE 3 Correlation of WPS FFT intensities with gene expression datasetsRName Category Type Description Correlations Rank Differences BH01 IH01IH02 IC15 IC20 IC17 IC37 IC35 healthy IC15 IC20 IC17 IC37 IC35 A.431Skin Skin cancer (Squamou s cells) Epidermoid carcinoma cell line -0.298-0.188 -0.149 -0.200 -0.140 -0.176 -0.195 -0.178 2 3 -9 -9 -12 -21 A549Lung Lung carcinoma Lung carcinoma cell line -0.289 -0.185 -0.144 -0.202-0.139 -0.172 -0.188 -0.170 3 -14 -12 -9 -2 -13 adipose_t issue PrimaryTissue Adipose tissue Primary tissue -0.270 -0.169 -0.137 -0.169 -0.121-0.153 -0.166 -0.148 1 12 5 0 14 12 adrenal_g land Primary TissueAdrenal gland Primary tissue -0.257 -0.158 -0.131 -0.173 -0.118 -0.145-0.161 -0.138 -2 -11 -5 1 5 8 AN3.CA Breast /Female Reproducti veUterine cancer Metastatic endometrial adenocarcin oma cell line -0.303-0.194 -0.157 -0.213 -0.147 -0.183 -0.195 -0.171 -4 -16 -13 -15 -8 -2appendix Primary Tissue Appendix Primary tissue -0.287 -0.185 -0.137-0.168 -0.118 -0.148 -0.171 -0.152 6 24 20 23 8 9 BEWO Other Uterinecancer Metastatic choriocarcin oma cell line -0.284 -0.184 -0.147 -0.193-0.139 -0.173 -0.194 -0.173 -5 3 -12 -15 -19 -27 bone_ marrow PrimaryTissue Bone marrow Primary tissue -0.343 -0.230 -0.165 -0.192 -0.142-0.167 -0.193 -0.165 2 40 9 30 16 28 CACO.2 Abdominal Colon adenocarcinoma Colon adenocarcin oma cell line -0.281 -0.177 -0.137 -0.192 -0.128-0.169 -0.184 -0.164 5 -5 -5 -14 -10 -9 CAPAN.2 Abdominal Pancreasadenocarci noma Pancreas adenocarcin oma cell line -0.291 -0.187 -0.145-0.202 -0.136 -0.176 -0.195 -0.175 3 -12 -2 -18 -19 -25 cerebral_ cortexPrimary Tissue Cerebral cortex Primary tissue -0.225 -0.136 -0.120-0.168 -0.108 -0.134 -0.142 -0.125 -1 -9 -3 0 0 0 colon Primary TissueColon Primary tissue -0.261 -0.162 -0.124 -0.164 -0.111 -0.145 -0.168-0.148 7 8 8 6 -7 1 Daudi Lymphoid Human Burkitt lymphoma Human Burkittlymphoma cell line -0.321 -0.206 -0.153 -0.195 -0.133 -0.165 -0.189-0.160 4 17 19 19 13 24 duodenu m Primary Tissue Duodenum Primary tissue-0.261 -0.164 -0.122 -0.159 -0.109 -0.144 -0.166 -0.144 10 10 10 7 -4 7EFO.21 Breast/Fe male Reproducti ve Ovarian cancer Metastatic ovarianserous cystadenoca rcinoma cell line -0.287 -0.186 -0.149 -0.201 -0.140-0.176 -0.188 -0.169 -7 -9 -14 -20 -1 -8 endometri um Primary TissueEndometri um Primary tissue -0.257 -0.158 -0.132 -0.178 -0.119 -0.151-0.166 -0.151 -3 -11 -4 -8 -3 -12 esophagu s Primary Tissue Esophagu sPrimary tissue -0.237 -0.147 -0.124 -0.156 -0.116 -0.141 -0.158 -0.145-3 1 -7 0 0 -7 fallopian_ tube Primary Tissue Fallopian tube Primarytissue -0.247 -0.157 -0.129 -0.171 -0.114 -0.145 -0.161 -0.145 -4 -13 -2-3 3 -2 gallbladd er Primary Tissue Gallbladde r Primary tissue -0.249-0.156 -0.119 -0.153 -0.103 -0.138 -0.154 -0.141 4 4 4 3 4 1 HaCaT SkinKeratinocy te cell line Keratinocyte cell line -0.290 -0.186 -0.149-0.194 -0.142 -0.173 -0.193 -0.173 -5 7 -18 -8 -8 -17 HDLM.2 LymphoidHodgkin lymphoma Hodgkin lymphoma cell line -0.316 -0.200 -0.154 -0.201-0.136 -0.173 -0.195 -0.171 1 6 11 1 -5 -5 heart_mu scle Primary TissueHeart muscle Primary tissue -0.246 -0.149 -0.126 -0.166 -0.113 -0.141-0.155 -0.140 -3 -3 -3 0 3 2 HEK_293 Other Kidney adrenal precursor cellline Embryonal kidney cell line, transformed by adenovirus type 5 -0.292-0.187 -0.150 -0.209 -0.139 -0.172 -0.189 -0.168 -4 -17 -4 -2 3 0 HELMyeloid Erythroleu kemia Erythroleuke mia cell line (AML M6 in relapseafter treatment for Hodgkin’s disease) -0.324 -0.205 -0.161 -0.210-0.140 -0.172 -0.194 -0.168 -1 -5 4 12 5 14 HeLa Breast/Fe maleReproducti ve Cervical cancer Cervical epithelial adenocarcin oma cellline -0.296 -0.186 -0.149 -0.203 -0.139 -0.172 -0.190 -0.171 1 -10 -5 -31 -8 Hep_G2 Abdominal Hepatocell ular carcinoma Hepatocellul arcarcinoma cell line -0.294 -0.186 -0.152 -0.202 -0.145 -0.186 -0.196-0.167 -4 -6 -18 -24 -17 2 HL.60 Myeloid Promyeloc ytic leukemia Acutepromyelocyti c leukemia (APL) cell line -0.332 -0.208 -0.161 -0.202-0.137 -0.171 -0.197 -0.170 2 8 18 18 -1 11 HMC.1 Myeloid Mastcellleukemia Mastcell leukemia cell line -0.337 -0.228 -0.165 -0.212 -0.149-0.181 -0.199 -0.180 0 -1 -2 3 0 -2 K.562 Lymphoid Leukemia Chronicmyeloid leukemia (CML) cell line -0.317 -0.202 -0.158 -0.211 -0.143-0.178 -0.195 -0.166 -3 -9 -5 -6 -1 13 Karpas.7 07 Lymphoid Multiplemyeloma Multiple myeloma cell line -0.325 -0.210 -0.155 -0.195 -0.136-0.167 -0.188 -0.164 4 20 18 22 21 22 kidney Primary Tissue KidneyPrimary tissue -0.245 -0.150 -0.130 -0.168 -0.119 -0.153 -0.171 -0.147-7 -4 -12 -19 -21 -6 liver Primary Tissue Liver Primary tissue -0.248-0.148 -0.122 -0.150 -0.110 -0.150 -0.164 -0.138 1 4 -1 -13 -4 3 lungPrimary Tissue Lung Primary tissue -0.264 -0.170 -0.133 -0.170 -0.121-0.148 -0.167 -0.149 3 4 0 7 3 6 lymph_no de Primary Tissue Lymph nodePrimary tissue -0.308 -0.195 -0.148 -0.182 -0.128 -0.155 -0.181 -0.156 724 17 25 14 22 MCF7 Breast/Fe male Reproducti ve Breast cancerMetastatic breast adenocarcin oma cell line -0.298 -0.195 -0.154 -0.207-0.145 -0.183 -0.196 -0.181 -3 -9 -12 -18 -11 -19 MOLT.4 LymphoidLeukemia (ALL) Acute lymphoblasti c leukemia (T-ALL) cell line -0.323-0.204 -0.163 -0.212 -0.144 -0.177 -0.197 -0.173 -3 -7 -2 -1 -5 -1 NB.4Myeloid Promyeloc ytic leukemia Acute promyelocyti c leukemia (APL) cellline -0.348 -0.228 -0.172 -0.211 -0.148 -0.182 -0.202 -0.171 0 4 3 5 213 NTERA.2 Urinary/Ma le Reproducti ve Urinary cancer Metastaticembryonal carcinoma cell line, cloned from TERA-2 -0.269 -0.170 -0.137-0.193 -0.117 -0.157 -0.169 -0.153 -2 -8 16 -2 5 0 ovary Primary TissueOvary Primary tissue -0.266 -0.162 -0.135 -0.181 -0.120 -0.152 -0.166-0.151 1 -7 2 -2 6 -1 pancreas Primary Tissue Pancreas Primary tissue-0.250 -0.159 -0.132 -0.170 -0.116 -0.150 -0.166 -0.150 -5 -5 1 -6 -5 -7PC.3 Urinary/Ma le Reproducti ve Prostate cancer Metastatic poorlydifferentiated prostate adenocarcin oma cell line -0.295 -0.190 -0.151-0.204 -0.138 -0.174 -0.188 -0.173 -3 -10 2 -6 8 -12 placenta PrimaryTissue Placenta Primary tissue -0.266 -0.166 -0.134 -0.168 -0.126 -0.151-0.166 -0.150 3 10 -7 1 9 6 prostate Primary Tissue Prostate Primarytissue -0.248 -0.161 -0.133 -0.175 -0.123 -0.150 -0.165 -0.151 -8 -10-11 -8 1 -12 rectum Primary Tissue Rectum Primary tissue -0.255 -0.154-0.117 -0.159 -0.102 -0.136 -0.161 -0.142 6 0 5 4 -2 0 REH LymphoidLeukemia (ALL) Pre-B cell leukemia cell line (ALL, first relapse) -0.330-0.216 -0.165 -0.214 -0.150 -0.182 -0.204 -0.174 -2 -5 -5 -2 -4 1 RH.30Sarcoma Rhabdomy osarcoma Metastatic rhabdomyos arcoma cell line -0.280-0.165 -0.137 -0.194 -0.125 -0.158 -0.175 -0.158 2 -14 -3 -7 -7 -7RPMI.822 6 Lymphoid Multiple Myeloma Multiple myeloma cell line -0.322-0.207 -0.155 -0.198 -0.138 -0.169 -0.190 -0.164 1 16 11 19 14 22 RT4Urinary/ Male Reproducti ve Bladder cancer Urinary bladder transitionalcell carcinoma cell line -0.282 -0.168 -0.145 -0.192 -0.136 -0.170-0.191 -0.171 -5 -1 -12 -16 -19 -25 salivary_g land Primary TissueSalivary gland Primary tissue -0.262 -0.166 -0.138 -0.177 -0.128 -0.154-0.172 -0.155 -7 2 -9 -2 -5 -5 SCLC.21 H Lung Small cell lung carcinomaSmall cell lung carcinoma cell line -0.259 -0.160 -0.138 -0.201 -0.123-0.157 -0.172 -0.146 -11 -31 -5 -12 -10 8 SH.SY5Y Brain Neuroblast omaMetastatic neuroblasto ma, clonal subline of neuroepitheli oma cell lineSK-N-SH -0.271 -0.170 -0.137 -0.201 -0.124 -0.157 -0.170 -0.151 2 -25 2-5 1 6 SiHa Breast/ Female Reproducti ve Cervical cancer Cervicalsquamous cell carcinoma cell line, integrated 1-2 copies of HPV16 -0.288-0.180 -0.148 -0.201 -0.139 -0.176 -0.193 -0.175 -2 -7 -15 -19 -11 -27SK.BR.3 Breast/ Female Reproducti ve Breast cancer Metastatic breastadenocarcin oma cell line -0.288 -0.176 -0.148 -0.195 -0.140 -0.176-0.191 -0.169 -3 -4 -21 -22 -12 -11 SK.MEL.3 0 Primary Tissue MelanomaMetastatic malignant melanoma cell line -0.301 -0.187 -0.154 -0.208-0.141 -0.174 -0.193 -0.171 -2 -12 -8 -3 -1 -6 skeletal_ muscle PrimaryTissue Skeletal muscle Primary tissue -0.261 -0.166 -0.134 -0.179 -0.125-0.150 -0.164 -0.145 -1 -7 -7 0 9 11 skin Skin Skin Primary tissue-0.259 -0.166 -0.134 -0.168 -0.127 -0.148 -0.167 -0.151 -4 8 -14 5 -1 -4small_ intestine Primary Tissue Small intestine Primary tissue -0.260-0.164 -0.121 -0.158 -0.107 -0.141 -0.166 -0.142 9 10 11 9 0 7 smooth_muscle Primary Tissue Smooth muscle Primary tissue -0.259 -0.158 -0.127-0.169 -0.113 -0.144 -0.161 -0.149 2 -6 3 4 4 -5 spleen Primary TissueSpleen Primary tissue -0.308 -0.202 -0.148 -0.180 -0.130 -0.155 -0.177-0.154 7 27 15 25 20 25 stomach Primary Tissue Stomach Primary tissue-0.264 -0.170 -0.131 -0.170 -0.117 -0.149 -0.169 -0.151 6 3 9 6 0 2testis Primary Tissue Testis Primary tissue -0.215 -0.142 -0.109 -0.147-0.093 -0.126 -0.133 -0.123 0 0 0 0 0 0 THP.1 Myeloid Monocytic leukemiaAcute monocytic leukemia (AML) cell line -0.338 -0.218 -0.168 -0.206-0.149 -0.182 -0.204 -0.176 -1 8 -1 1 -3 0 thyroid_ gland Primary TissueThyroid gland Primary tissue -0.261 -0.158 -0.136 -0.178 -0.121 -0.153-0.170 -0.161 -2 -7 -2 -6 -6 -19 TIME Other Microvasc ular endothelialcell line Telomerase -immortalized human microvascula r endothelialcells (pooled) -0.296 -0.180 -0.147 -0.198 -0.134 -0.170 -0.186 -0.170 5-3 3 -1 3 -11 tonsil Primary Tissue Tonsil Primary tissue -0.282 -0.179-0.141 -0.169 -0.125 -0.147 -0.173 -0.152 -1 20 8 23 4 9 U.138_ MG BrainGlioblasto ma Glioblastoma cell line -0.288 -0.177 -0.144 -0.191 -0.126-0.162 -0.177 -0.161 1 8 7 0 2 2 U.2_OS Sarcoma Osteosarc oma Osteosarcoma cell line -0.275 -0.175 -0.139 -0.192 -0.134 -0.159 -0.170 -0.160 -20 -11 -3 6 -3 U.2197 Sarcoma Sarcoma Malignant fibrous histiocytoma cellline -0.290 -0.181 -0.146 -0.195 -0.129 -0.164 -0.180 -0.165 2 1 5 3 4 0U.251_ MG Brain Glioblasto ma Glioblastoma cell line -0.292 -0.178-0.140 -0.197 -0.125 -0.160 -0.177 -0.165 9 -6 11 4 4 -4 U.266.70Lymphoid Multiple Myeloma Multiple myeloma cell line (1970,IL-6-dependent) -0.320 -0.207 -0.157 -0.202 -0.135 -0.170 -0.191 -0.165-1 4 19 15 12 17 U.266.84 Lymphoid Multiple Myeloma Multiple myelomacell line (1984, in vitro differentiated ) -0.326 -0.212 -0.162 -0.207-0.139 -0.175 -0.194 -0.169 -1 2 11 8 10 14 U.698 Lymphoid B-celllymphoma B-cell lymphoma cell line (lymphoblast ic lymphosarco ma)-0.328 -0.212 -0.159 -0.203 -0.137 -0.170 -0.194 -0.166 2 5 18 20 6 20U.87_MG Brain Glioblasto ma, astrocytom a Glioblastoma , astrocytomacell line -0.285 -0.175 -0.143 -0.192 -0.127 -0.160 -0.174 -0.162 1 0 2-2 2 -4 U.937 Myeloid Myelomon ocytic histiocytic lymphoma Myelomonocytic histiocytic lymphoma cell line -0.346 -0.224 -0.167 -0.201 -0.146-0.180 -0.199 -0.173 1 18 3 5 2 6 urinary_ bladder Primary TissueUrinary bladder Primary tissue -0.260 -0.158 -0.130 -0.165 -0.118 -0.146-0.164 -0.150 3 5 -2 1 3 -6 WM.115 Skin Melanoma Malignant melanoma cellline -0.284 -0.175 -0.144 -0.193 -0.130 -0.160 -0.178 -0.157 -1 -4 -4 -3-3 2

Correlation values between average FFT (fast Fourier Transformation)intensities for the 193-199 bp frequencies in the first 10 kb downstreamof the transcriptional start site with FPKM expression values measuredfor 19,378 Ensembl gene identifiers in 44 human cell lines and 32primary tissues by the Human Protein Atlas. Table 3 also contains briefdescriptions for each of the expression samples as provided by theProtein Atlas as well as rank transformations and rank differences tothe IH01, IH02 and BH01 samples.

EXAMPLE 5: Determining Non-healthy Tissue(s)-of-origin From cfDNA

To test whether additional contributing tissues in non-healthy statesmight be inferred, cfDNA samples obtained from five late-stage cancerpatients were sequenced. The patterns of nucleosome spacing in thesesamples revealed additional contributions to cfDNA that correlated moststrongly with non-hematopoietic tissues or cell lines, often matchingthe anatomical origin of the patient’s cancer.

Nucleosome Spacing in Cancer Patients’ cfDNA IdentifiesNon-Hematopoietic Contributions

To determine whether signatures of non-hematopoietic lineagescontributing to circulating cfDNA in non-healthy states could bedetected, 44 plasma samples from individuals with clinical diagnoses ofa variety of Stage IV cancers were screened with light sequencing ofsingle-stranded libraries prepared from cfDNA (Table 4; median 2.2-foldcoverage):

TABLE 4 Clinical diagnoses and cfDNA yield for cancer panel Sample IDClinical Dx Stage cfDNA Yield (ng/ml) Patient Sex IC01 † Kidney cancer(Transitional cell) IV 242 F IC02 Ovarian cancer (undefined) IV 22.5 FIC03 Skin cancer (Melanoma) IV 12.0 M IC04 Breast cancer(Invasive/infiltrating ductal) IV 12.6 F IC05 Lung cancer(Adenocarcinoma) IV 5.4 M IC06 Lung cancer (Mesothelioma) IV 11.4 M IC07† Gastric cancer (undefined) IV 52.2 M IC08 Uterine cancer (undefined)IV 15.0 F IC09 Ovarian cancer (serous tumors) IV 8.4 F IC10 Lung cancer(adenocarcinoma) IV 11.4 F IC11 Colorectal cancer (undefined) IV 11.4 MIC12 Breast cancer (Invasive/infiltrating lobular) IV 12.0 F IC13Prostate cancer (undefined) IV 12.3 M IC14 Head and neck cancer(undefined) IV 27.0 M IC15 § Lung cancer (Small cell) IV 22.5 M IC16Bladder cancer (undefined) IV 14.1 M IC17 § Liver cancer (Hepatocellularcarcinoma) IV 39.0 M IC18 Kidney cancer (Clear cell) IV 10.5 F IC19Testicular cancer (Seminomatous) IV 9.6 M IC20 § Lung cancer (Squamouscell carcinoma) IV 21.9 M IC21 Pancreatic cancer (Ductal adenocarcinoma)IV 35.4 M IC22 Lung cancer (Adenocarcinoma) IV 11.4 F IC23 Liver cancer(Hepatocellular carcinoma) IV 17.1 M IC24 Pancreatic cancer (Ductaladenocarcinoma) IV 37.2 M IC25 Pancreatic cancer (Ductal adenocarcinoma)IV 27.9 M IC26 Prostate cancer (Adenocarcinoma) IV 24.6 M IC27 Uterinecancer (undefined) IV 19.2 F IC28 Lung cancer (Squamous cell carcinoma)IV 33.3 M IC29 Head and neck cancer (undefined) IV 14.4 M IC30Esophageal cancer (undefined) IV 10.5 M IC31 † Ovarian cancer(undefined) IV 334.8 F IC32 Lung cancer (Small cell) IV 9.6 F IC33Colorectal cancer (Adenocarcinoma) IV 13.8 M IC34 Breast cancer(Invasive/infiltrating lobular) IV 33.6 F IC35 § Breast cancer (Ductalcarcinoma in situ) IV 16.2 F IC36 Liver cancer (undefined) IV 26.4 MIC37 § Colorectal cancer (Adenocarcinoma) IV 15.9 F IC38 Bladder cancer(undefined) IV 6.6 M IC39 Kidney cancer (undefined) IV 39.0 M IC40Prostate cancer (Adenocarcinoma) IV 13.8 M IC41 Testicular cancer(Seminomatous) IV 16.5 M IC42 Lung cancer (Adenocarcinoma) IV 11.4 FIC43 Skin cancer (Melanoma) IV 21.9 F IC44 Esophageal cancer (undefined)IV 25.8 F IC45 t Colorectal cancer (Adenocarcinoma) IV 3.0 M IC46 **Breast cancer (Ductal carcinoma in situ) IV 36.6 F IC47 Pancreaticcancer (Ductal adenocarcinoma) IV 19.2 F IC48 ** Breast cancer(Invasive/infiltrating lobular) IV 13.8 F §: sample was selected foradditional sequencing. **: only 0.5 ml of plasma was available for thissample. †: sample failed QC and was not used for further analysis.

Table 4 shows clinical and histological diagnoses for 48 patients fromwhom plasma-borne cfDNA was screened for evidence of high tumor burden,along with total cfDNA yield from 1.0 ml of plasma from each individualand relevant clinical covariates. Of these 48, 44 passed QC and hadsufficient material. Of these 44, five were selected for deepersequencing. cfDNA yield was determined by Qubit Fluorometer 2.0 (LifeTechnologies).

These samples were prepared with the same protocol and many in the samebatch as IH02 of Example 4. Human peripheral blood plasma for 52individuals with clinical diagnosis of Stage IV cancer (Table 4) wasobtained from Conversant Bio or PlasmaLab International (Everett,Washington, USA) and stored in 0.5 ml or 1 ml aliquots at -80° C. untiluse. Human peripheral blood plasma for four individuals with clinicaldiagnosis of systemic lupus erythematosus was obtained from ConversantBio and stored in 0.5 ml aliquots at -80° C. until use. Frozen plasmaaliquots were thawed on the bench-top immediately before use.Circulating cell-free DNA was purified from 2 ml of each plasma samplewith the QiaAMP Circulating Nucleic Acids kit (Qiagen) as per themanufacturer’s protocol. DNA was quantified with a Qubit fluorometer(Invitrogen). To verify cfDNA yield in a subset of samples, purified DNAwas further quantified with a custom qPCR assay targeting a multicopyhuman Alu sequence; the two estimates were found to be concordant.

Because matched tumor genotypes were not available, each sample wasscored on two metrics of aneuploidy to identify a subset likely tocontain a high proportion of tumor-derived cfDNA: first, the deviationfrom the expected proportion of reads derived from each chromosome (FIG.62A); and second, the per-chromosome allele balance profile for a panelof common single nucleotide polymorphisms (FIG. 62B). Based on thesemetrics, single-stranded libraries derived from five individuals (with asmall cell lung cancer, a squamous cell lung cancer, a colorectaladenocarcinoma, a hepatocellular carcinoma, and a ductal carcinoma insitu breast cancer) were sequenced to a depth similar to that of IH02 inExample 4 (Table 5; mean 30-fold coverage):

TABLE 5 Sequencing statistics for additional samples included in CA01set Sample name Library type Reads Fragments sequenced Aligned AlignedQ30 Coverage Est. % duplicates 35-80bp 120-180bp IH03 SSP 2x39 5329285592.66% 72.37% 2.29 15.46% 11.05% 52.34% IP01 † DSP 2x101, 2x1021214536629 97.22% 86.38% 76.11 0.55% 0.08% 62.77% IP02 t DSP 2x101,2x102 855040273 97.16% 87.72% 52.46 0.83% 0.07% 68.10% IA01 SSP 2x3953934607 87.42% 68.30% 2.02 22.70% 15.20% 49.77% IA02 SSP 2x39 4249622295.42% 76.61% 1.95 4.74% 12.28% 59.00% IA03 SSP 2x39 51278489 93.12%71.33% 2.05 25.68% 14.27% 52.57% IA04 SSP 2x39 50768476 90.30% 70.51%2.14 7.83% 17.80% 36.76% IA05 DSP 2x101 194985271 98.80% 90.61% 11.0912.05% 2.24% 71.67% IA06 DSP 2x101 171670054 98.90% 90.88% 9.90 5.41%1.93% 71.26% IA07 DSP 2x101 208609489 98.67% 90.34% 11.69 11.45% 2.59%74.84% IA08 DSP 2x101 193729556 98.81% 90.70% 10.84 11.96% 2.58% 76.24%IC02 SSP 2x39 57913605 95.07% 75.57% 2.59 5.40% 12.98% 60.00% IC03 SSP2x39 63862631 95.78% 75.66% 2.79 8.32% 13.25% 62.20% IC04 SSP 2x3955239248 95.47% 76.26% 2.57 8.28% 10.98% 58.48% IC05 SSP 2x39 3962385089.80% 69.92% 1.60 9.24% 14.63% 50.33% IC06 SSP 2x39 59679981 95.57%74.90% 2.11 3.93% 24.30% 41.46% IC08 SSP 2x39 46933688 94.38% 74.21%1.92 5.92% 16.04% 45.25% IC09 SSP 2x42 59639583 91.22% 71.15% 2.13 6.69%21.39% 43.50% IC10 SSP 2x42 53994406 93.73% 73.40% 1.83 2.00% 27.08%37.62% IC11 SSP 2x42 59225460 93.25% 72.51% 2.15 5.26% 21.30% 43.33%IC12 SSP 2x42 57884742 93.52% 74.33% 2.34 2.66% 18.28% 46.58% IC13 SSP2x42 71946779 92.94% 72.47% 2.52 2.18% 23.51% 43.97% IC14 SSP 2x4261649203 94.54% 73.47% 2.20 3.23% 22.26% 43.37% IC15 SSP x50, 43/42908512803 95.49% 76.83% 29.77 10.66% 25.42% 38.47% IC16 SSP 2x4262739733 92.81% 72.85% 2.47 2.77% 17.71% 48.04% IC17 SSP 2x50, 2x391072374044 96.02% 76.42% 42.08 12.16% 17.08% 50.02% IC18 SSP 2x3959976914 87.91% 68.67% 2.24 4.39% 18.85% 44.44% IC19 SSP 2x39 5144714989.38% 69.39% 2.02 8.24% 17.30% 46.33% IC20 SSP 2x50, 2x39 64083854096.30% 79.11% 23.38 12.43% 25.72% 39.87% IC21 SSP 2x39 53000679 94.64%74.57% 1.79 37.39% 29.89% 43.81% IC22 SSP 2x39 58102606 94.08% 74.08%2.51 6.24% 13.65% 58.41% IC23 SSP 2x39 65859970 95.67% 75.67% 2.94 5.34%11.09% 60.85% IC24 SSP 43/42 66344431 94.63% 74.46% 2.48 2.00% 22.46%46.31% IC25 SSP 43/42 75066833 93.75% 73.66% 2.86 2.24% 21.30% 46.19%IC26 SSP 43/42 79180860 92.59% 72.32% 2.97 2.93% 22.34% 40.42% IC27 SSP43/42 78037377 88.81% 67.04% 2.20 1.50% 31.31% 30.59% IC28 SSP 43/4261402081 95.24% 75.74% 2.60 2.46% 18.71% 46.44% IC29 SSP 2x39 4998952294.46% 73.36% 1.75 3.03% 25.82% 36.23% IC30 SSP 2x39 58439504 93.52%71.19% 1.75 17.35% 29.58% 30.47% IC32 SSP 43/42 78233981 87.86% 66.80%2.25 1.79% 30.12% 31.20% IC33 SSP 43/42 62196185 87.26% 66.71% 1.931.93% 27.44% 36.92% IC34 SSP 43/42 63572169 95.42% 76.74% 2.53 2.35%19.64% 48.55% IC35 SSP 43/42 618554393 86.47% 65.90% 18.22 5.23% 28.18%35.24% IC36 SSP 43/42 54402943 94.62% 74.73% 2.21 3.32% 17.02% 52.42%IC37 SSP 2x50, 43/42 1175553677 93.00% 74.46% 38.22 10.15% 28.47% 35.11%IC38 SSP 43/42 47981963 89.35% 69.45% 1.78 6.47% 18.59% 43.03% IC39 SSP43/42 61968854 95.29% 75.57% 2.62 2.54% 14.42% 57.28% IC40 SSP 2x3953228209 93.54% 71.69% 1.81 8.85% 24.88% 34.95% IC41 SSP 43/42 7808165587.11% 65.25% 2.26 1.61% 27.94% 35.21% IC42 SSP 2x39 53017317 93.59%74.33% 2.02 10.74% 19.04% 44.12% IC43 SSP 43/42 76395478 88.41% 67.21%2.40 1.56% 26.68% 37.76% IC44 SSP 43/42 61354307 95.15% 74.88% 2.454.34% 19.10% 46.39% IC46 SSP 2x39 60123123 94.51% 72.23% 2.13 10.37%15.46% 50.93% IC47 SSP 2x39 59438172 95.58% 73.84% 2.07 9.33% 21.67%43.34% IC48 SSP 43/42 55704417 91.35% 72.79% 2.01 13.87% 22.56% 38.68%IC49 DSP 2x101 170489015 99.02% 90.53% 11.19 5.93% 2.41% 59.93% IC50 DSP2x101 203828224 98.72% 90.28% 10.82 2.83% 4.81% 66.23% IC51 DSP 2x101200454421 98.63% 90.53% 11.77 9.50% 2.58% 67.04% IC52 DSP 2x101186975845 98.97% 91.25% 11.37 2.57% 0.83% 68.96% SSP, single-strandedlibrary preparation protocol. DSP, double-stranded library preparationprotocol. t Sample has been previously published (J.O. Kitzman et al.,Science Translational Medicine (2012)).

Table 5 tabulates sequencing-related statistics, including the totalnumber of fragments sequenced, read lengths, the percentage of suchfragments aligning to the reference with and without a mapping qualitythreshold, mean coverage, duplication rate, and the proportion ofsequenced fragments in two length bins, for each sample. Fragment lengthwas inferred from alignment of paired-end reads. Due to the short readlengths, coverage was calculated by assuming the entire fragment hadbeen read. The estimated number of duplicate fragments is based onfragment endpoints, which may overestimate the true duplication rate inthe presence of highly stereotyped cleavage.

As described above, FFT was performed on the long fragment WPS valuesacross gene bodies and correlated the average intensity in the 193-199bp frequency range against the same 76 expression datasets for humancell lines and primary tissues. In contrast with the three samples fromhealthy individuals from Example 4 (where all of the top 10, and nearlyall of the top 20, correlations were to lymphoid or myeloid lineages),many of the most highly ranked cell lines or tissues representnon-hematopoietic lineages, in some cases aligning with the cancer type(FIG. 61 ; Table 3). For example, for IC17, where the patient had ahepatocellular carcinoma, the top-ranked correlation was with HepG2, ahepatocellular carcinoma cell line. For IC35, where the patient had aductal carcinoma in situ breast cancer, the top-ranked correlation waswith MCF7, a metastatic breast adenocarcinoma cell line. In other cases,the cell lines or primary tissues that exhibit the greatest change incorrelation rank aligned with the cancer type. For example, for IC15,where the patient had small cell lung cancer, the largest change incorrelation rank (-31) was for a small cell lung cancer cell line(SCLC-21H). For IC20 (a lung squamous cell carcinoma) and IC35 (acolorectal adenocarcinoma), there were many non-hematopoietic cancercell lines displacing the lymphoid/myeloid cell lines in terms ofcorrelation rank, but the alignment of these to the specific cancer typewas less clear. It is possible that the specific molecular profile ofthese cancers was not well-represented amongst the 76 expressiondatasets (e.g., none of these are lung squamous cell carcinomas; CACO-2is a cell line derived from a colorectal adenocarcinoma, but is known tobe highly heterogeneous).

A greedy, iterative approach was used to estimate the proportions ofvarious cell-types and/or tissues contributing to cfDNA derived from thebiological sample. First, the cell-type or tissue whose reference map(here, defined by the 76 RNA expression datasets) had the highestcorrelation with the average FFT intensity in the 193-199 bp frequencyof the WPS long fragment values across gene bodies for a given cfDNAsample was identified. Next, a series of “two tissue” linear mixturemodels were fitted, including the cell-type or tissue with the highestcorrelation as well as each of the other remaining cell-types or tissuesfrom the full set of reference maps. Of the latter set, the cell-type ortissue with the highest coefficient was retained as contributory, unlessthe coefficient was below 1% in which case the procedure was terminatedand this last tissue or cell-type not included. This procedure wasrepeated, i.e. “three-tissue”, “four-tissue”, and so on, untiltermination based on the newly added tissue being estimated by themixture model to contribute less than 1%. The mixture model takes theform:argmax_{a,b,c,...} cor(Mean_FFTintensity_193-199, a*log2ExpTissue1 +b*log2Tissue2 + c*log2Tissue3 + ... + (1-a-b-c-...)*log2ExpTissueN).For example, for IC17, a cfDNA sample derived from a patient withadvanced hepatocellular carcinoma, this procedure predicted 9contributory cell types, including Hep_G2 (28.6%), HMC.1 (14.3%), REH(14.0%), MCF7 (12.6%), AN3.CA (10.7%), THP.1 (7.4%), NB.4 (5.5%),U.266.84 (4.5%), and U.937 (2.4%). For BH01, a cfDNA samplecorresponding to a mixture of healthy individuals, this procedurepredicted 7 contributory cell types or tissues, including bone marrow(30.0%), NB.4 (19.6%), HMC.1 (13.9%), U.937 (13.4%), U.266.84 (12.5%),Karpas.707 (6.5%), and REH (4.2%). Of note, for IC17, the sample derivedfrom a cancer patient, the highest proportion of predicted contributioncorresponds to a cell line that is closely associated with the cancertype that is present in the patient from whom this cfDNA was derived(Hep_G2 and hepatocellular carcinoma). In contrast, for BH01, thisapproach predicts contributions corresponding only to tissues or celltypes that are primarily associated with hematopoiesis, the predominantsource of plasma cfDNA in healthy individuals.

EXAMPLE 6: General Methods for Examples 4-5 Samples

Bulk human peripheral blood plasma, containing contributions from anunknown number of healthy individuals, was obtained from STEMCELLTechnologies (Vancouver, British Columbia, Canada) and stored in 2 mlaliquots at -80° C. until use. Individual human peripheral blood plasmafrom anonymous, healthy donors was obtained from Conversant Bio(Huntsville, Alabama, USA) and stored in 0.5 ml aliquots at -80° C.until use.

Whole blood from pregnant women IP01 and IP02 was obtained at 18 and 13gestational weeks, respectively, and processed as previouslydescribed41.

Human peripheral blood plasma for 52 individuals with clinical diagnosisof Stage IV cancer (Supplementary Table 4) was obtained from ConversantBio or PlasmaLab International (Everett, Washington, USA) and stored in0.5 ml or 1 ml aliquots at -80° C. until use. Human peripheral bloodplasma for four individuals with clinical diagnosis of systemic lupuserythematosus was obtained from Conversant Bio and stored in 0.5 mlaliquots at -80° C. until use.

Processing of Plasma Samples

Frozen plasma aliquots were thawed on the bench-top immediately beforeuse. Circulating cell-free DNA was purified from 2 ml of each plasmasample with the QiaAMP Circulating Nucleic Acids kit (Qiagen) as per themanufacturer’s protocol. DNA was quantified with a Qubit fluorometer(Invitrogen). To verify cfDNA yield in a subset of samples, purified DNAwas further quantified with a custom qPCR assay targeting a multicopyhuman Alu sequence; the two estimates were found to be concordant.

Preparation of Double-Stranded Sequencing Libraries

Barcoded sequencing libraries were prepared with the ThruPLEX-FD orThruPLEX DNA-seq 48D kits (Rubicon Genomics), comprising a proprietaryseries of end-repair, ligation, and amplification reactions. Between 0.5ng and 30.0 ng of cfDNA were used as input for all clinical samplelibraries. Library amplification for all samples was monitored byreal-time PCR to avoid over-amplification, and was typically terminatedafter 4-6 cycles.

Preparation of Single-Stranded Sequencing Libraries

Adapter 2 was prepared by combining 4.5 µl TE (pH 8), 0.5 µl 1 M NaCl,10 µl 500 uM oligo Adapter2.1, and 10 µl 500 µM oligo Adapter2.2,incubating at 95° C. for 10 seconds, and decreasing the temperature to14° C. at a rate of 0.1° C./s. Purified cfDNA fragments weredephosphorylated by combining 2x CircLigase II buffer (Epicentre), 5 mMMnCl₂, and 1U FastAP alkaline phosphatase (Thermo Fisher) with 0.5-10 ngfragments in a 20 µl reaction volume and incubating at 37° C. for 30minutes. Fragments were then denatured by heating to 95° C. for 3minutes, and were immediately transferred to an ice bath. The reactionwas supplemented with biotin-conjugated adapter oligo CL78 (5 pmol), 20%PEG-6000 (w/v), and 200U CircLigase II (Epicentre) for a total volume of40 µl, and was incubated overnight with rotation at 60° C., heated to95° C. for 3 minutes, and placed in an ice bath. For each sample, 20 µlMyOne C1 beads (Life Technologies) were twice washed in bead bindingbuffer (BBB) (10 mM Tris-HCl [pH 8], 1 M NaCl, 1 mM EDTA [pH 8], 0.05%Tween-20, and 0.5% SDS), and resuspended in 250 µl BBB. Adapter-ligatedfragments were bound to the beads by rotating for 60 minutes at roomtemperature. Beads were collected on a magnetic rack and the supernatantwas discarded. Beads were washed once with 500 ul wash buffer A (WBA)(10 mM Tris-HCl [pH 8], 1 mM EDTA [pH 8], 0.05% Tween-20, 100 mM NaCl,0.5% SDS) and once with 500 µl wash buffer B (WBB) (10 mM Tris-HCl [pH8], 1 mM EDTA [pH 8], 0.05% Tween-20, 100 mM NaCl). Beads were combinedwith 1X Isothermal Amplification Buffer (NEB), 2.5 µM oligo CL9, 250 µM(each) dNTPs, and 24U Bst 2.0 DNA Polymerase (NEB) in a reaction volumeof 50 µl, incubated with gentle shaking by ramping temperature from 15°C. to 37° C. at 1° C./minute, and held at 37° C. for 10 minutes. Aftercollection on a magnetic rack, beads were washed once with 200 µl WBA,resuspended in 200 µl of stringency wash buffer (SWB) (0.1X SSC, 0.1%SDS), and incubated at 45° C. for 3 minutes. Beads were again collectedand washed once with 200 µl WBB. Beads were then combined with 1XCutSmart Buffer (NEB), 0.025% Tween-20, 100 µM (each) dNTPs, and 5U T4DNA Polymerase (NEB) and incubated with gentle shaking for 30 minutes atroom temperature. Beads were washed once with each of WBA, SWB, and WBBas described above. Beads were then mixed with 1X CutSmart Buffer (NEB),5% PEG-6000, 0.025% Tween-20, 2 µM double-stranded adapter 2, and 10U T4DNA Ligase (NEB), and incubated with gentle shaking for 2 hours at roomtemperature. Beads were washed once with each of WBA, SWB, and WBB asdescribed above, and resuspended in 25 µl TET buffer (10 mM Tris-HCl [pH8], 1 mM EDTA [pH 8], 0.05% Tween-20). Second strands were eluted frombeads by heating to 95° C., collecting beads on a magnetic rack, andtransferring the supernatant to a new tube. Library amplification forall samples was monitored by real-time PCR to avoid over-amplification,and required an average of 4 to 6 cycles per library.

Sequencing

All libraries were sequenced on HiSeq 2000 or NextSeq 500 instruments(Illumina).

Primary Sequencing Data Processing

Barcoded paired end (PE) Illumina sequencing data was split allowing upto one substitution in the barcode sequence. Reads shorter or equal toread length were consensus called and adapter trimmed. Remainingconsensus single end reads (SR) and the individual PE reads were alignedto the human reference genome sequence (GRCh37, 1000 Genomes phase 2technical reference downloaded from ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/reference/phase2_referenceassembly_ sequence/) using the ALN algorithm implemented in BWA v0.7.10.PE reads were further processed with BWA SAMPE to resolve ambiguousplacement of read pairs or to rescue missing alignments by a moresensitive alignment step around the location of one placed read end.Aligned SR and PE data was directly converted to sorted BAM format usingthe SAMtools API. BAM files of the sample were merged across lanes andsequencing runs.

Quality control was performed using FastQC (v0.11.2), obtaining alibrary complexity estimate (Picard tools v1.113), determining theproportion of adapter dimers, the analysis of the inferred libraryinsert size, the nucleotide and dinucleotide frequencies at the outerreads ends as well as checking the mapping quality distributions of eachlibrary.

Simulated Read Data Sets

Aligned sequencing data was simulated (SR if shorter than 45 bp, PE 45bp otherwise) for all major chromosomes of the human reference (GRC37h).For this purpose, dinucleotide frequencies were determined from realdata on both read ends and both strand orientations. Dinucleotidefrequencies were also recorded for the reference genome on both strands.Further, the insert size distribution of the real data was extracted forthe 1-500 bp range. Reads were simulated by iterating through thesequence of the major reference chromosomes. At each step (i.e., one ormore times at each position depending on desired coverage), (1) thestrand is randomly chosen, (2) the ratio of the dinucleotide frequencyin the real data over the frequency in the reference sequence is used torandomly decide whether the initiating dinucleotide is considered, (3)an insert size is sampled from the provided insert-size distribution and(4) the frequency ratio of the terminal dinucleotide is used to randomlydecide whether the generated alignment is reported. The simulatedcoverage was matched to that of the original data after PCR duplicateremoval.

Coverage, Read Starts and Window Protection Scores

The data of the present disclosure provides information about the twophysical ends of DNA molecules used in sequencing library preparation.We extract this information using the SAMtools application programminginterface (API) from BAM files. As read starts, we use both outeralignment coordinates of PE data for which both reads aligned to thesame chromosome and where reads have opposite orientations. In caseswhere PE data was converted to single read data by adapter trimming, weconsider both end coordinates of the SR alignment as read starts. Forcoverage, we consider all positions between the two (inferred) moleculeends, including these end positions. We define windowed protectionscores (WPS) of a window size k as the number of molecules spanning awindow minus those starting at any bases encompassed by the window. Weassign the determined WPS to the center of the window. For molecules inthe 35-80 bp range (short fraction), we use a window size of 16 and, formolecules in the 120-180 bp (long fraction), we use a window size of120.

Nucleosome Peak Calling

Local maxima of nucleosome protection are called from the long fractionWPS, which we locally adjust to a running median of zero (1 kb window)and smooth using a Savitzky-Golay filter (window size 21, 2nd orderpolynomial). The WPS track is then segmented into above zero regions(allowing up to 5 consecutive positions below zero). If the resultingregion is between 50-150 bp long, we identify the median value of thatregion and search for the maximum-sum contiguous window above themedian. We report the start, end and center coordinates of this window.Peak-to-peak distances, etc., are calculated from the centercoordinates. The score of the call is determined as the distance betweenmaximum value in the window and the average of the two adjacent WPSminima neighboring the region. If the identified region is 150-450 bplong, we apply the same above median contiguous window approach, butonly report those windows that are between 50-150 bp in size. For scorecalculation of multiple windows derived from the 150-450 bp regions, weassume the neighboring minima within the region to be zero. We discardregions shorter than 50 bp and longer than 450 bp.

Dinucleotide Composition of 167 bp Fragments

Fragments with inferred lengths of exactly 167 bp, corresponding to thedominant peak of the fragment size distribution, were filtered withinsamples to remove duplicates. Dinucleotide frequencies were calculatedin a strand-aware manner, using a sliding 2 bp window and referencealleles at each position, beginning 50 bp upstream of one fragmentendpoint and ending 50 bp downstream of the other endpoint. Observeddinucleotide frequencies at each position were compared to expecteddinucleotide frequencies determined from a set of simulated readsreflecting the same cleavage biases calculated in a library-specificmanner (see above for details).

WPS Profiles Surrounding Transcription Factor Binding Sites and GenomicFeatures

Analysis began with an initial set of clustered FIMO (motif-based)intervals defining a set of computationally predicted transcriptionfactor binding sites. For a subset of clustered transcription factors(AP-2-2, AP-2, CTCF_Core-2, E2F-2, EBF1, Ebox-CACCTG, Ebox, ESR1, ETS,IRF-2, IRF-3, IRF, MAFK, MEF2A-2, MEF2A, MYC-MAX, PAX5-2, RUNX2,RUNX-AML, STAF-2, TCF-LEF, YY1), the set of sites was refined to a moreconfident set of actively bound transcription factor binding sites basedon experimental data. For this purpose, only predicted binding sitesthat overlap with peaks defined by ChlP-seq experiments from publicallyavailable ENCODE data (TfbsClusteredV3 set downloaded from UCSC) wereretained.

Windowed protection scores surrounding these sites were extracted forboth the CH01 sample and the corresponding simulation. A protectionscore for each site/feature was calculated at each position relative tothe start coordinate of each binding site and the aggregated. Plots ofCTCF binding sites were shifted such that the zero coordinate on thex-axis at the center of the known 52 bp binding footprint of CTCF. Themean of the first and last 500 bp (which is predominantly flat andrepresents a mean offset) of the 5 kb extracted WPS signal was thensubtracted from the original signal. For long fragment signal only, asliding window mean was calculated using a 200 bp window and subtractedfrom the original signal. Finally, the corrected WPS profile for thesimulation was subtracted from the corrected WPS profile for CH01 tocorrect for signal that was a product of fragment length and ligationbias. This final profile was plotted and termed the “Adjusted WPS”.

Genomic features, such as transcription start sites, transcription endsites, start codons, splice donor, and splice acceptor sites wereobtained from Ensembl Build version 75. Adjusted WPS surrounding thesefeatures was calculated and plotted as described above for transcriptionfactor binding sites.

Analysis of Nucleosome Spacing Around CTCF Binding Sites andCorresponding WPS

CTCF sites used for this analysis first included clustered FIMOpredictions of CTCF binding sites (computationally predicted viamotifs). We then created two additional subsets of this set: 1)intersection with the set of CTCF ChlP-seq peaks available through theENCODE TfbsClusteredV3 (see above), and 2) intersection with a set ofCTCF sites that are experimentally observed to be active across 19tissues.

The positions of 10 nucleosomes on either side of the binding site wereextracted for each site. We calculated distances between all adjacentnucleosomes to obtain a distribution of inter-nucleosome distances foreach set of sites. The distribution of -1 to +1 nucleosome spacingchanged substantially, shifting to larger spacing, particularly in the230-270 bp range. This suggested that truly active CTCF sites largelyshift towards wider spacing between the -1 and +1 nucleosomes, and thata difference in WPS for both long and short read fractions mighttherefore be apparent. Therefore, the mean short and long fragment WPSat each position relative to the center of CTCF sites were additionallycalculated. To explore the effect of nucleosome spacing, this mean wastaken within bins of -1 to +1 nucleosome spacing of less than 160,160-200, 200-230, 230-270, 270-420, 420-460, and greater than 420 bp.These intervals approximately captured spacings of interest, such as thedominant peak and the emerging peak at 230-270 bp for more confidentlyactive sites.

Analysis of DNase I Hypersensitive Sites (DHS)

DHS peaks for 349 primary tissue and cell line samples in BED format byMaurano et al. (Science, vol. 337(6099), pp. 1190-95 (2012);“all_fdr0.05_hot” file, last modified Feb. 13, 2012) were downloadedfrom the University of Washington Encode database. Samples derived fromfetal tissues, comprising 233 of these peak sets, were removed from theanalysis as they behaved inconsistently within tissue type, possiblybecause of unequal representation of multiple cell types within eachtissue sample. 116 samples representing a variety of cell lineages wereretained for analysis. For the midpoint of each DHS peak in a particularset, the nearest upstream and downstream calls in the CH01 callset wereidentified, and the genomic distance between the centers of those twocalls was calculated. The distribution of all such distances wasvisualized for each DHS peak callset using a smoothed density estimatecalculated for distances between 0 and 500 bp.

Gene Expression Analysis

FPKM expression values, measured for 20,344 Ensembl gene identifiers in44 human cell lines and 32 primary tissues by the Human Protein Atlas(“ma.csv” file) were used in this study. For analyses across tissues,genes with less than 3 non-zero expression values were excluded (19,378genes passing this filter). The expression data set was provided withone decimal precession for the FPKM values. Thus, a zero expressionvalue (0.0) indicates expression between 0 and a value less than 0.05.Unless otherwise noted, the minimum expression value was set to 0.04FPKM before log₂-transformation of the expression values.

Smooth Periodograms and Smoothing of Trajectories

The long fragment WPS was used to calculate periodograms of genomicregions using Fast Fourier Transform (FFT, spec.pgram in the Rstatistical programming environment) with frequencies between 1/500bases and 1/100 bases. Parameters to smooth (3 bp Daniell smoother;moving average giving half weight to the end values) and de-trend thedata (i.e. subtract the mean of the series and remove a linear trend)are optionally additionally used.

Where indicated, the recursive time series filter as implemented in theR statistical programming environment was used to remove high frequencyvariation from trajectories. 24 filter frequencies (1/seq(5,100,4)) wereused, and the first 24 values of the trajectory as initial values wereused. Adjustments for the 24-value shift in the resulting trajectorieswere made by repeating the last 24 values of the trajectory.

Correlation of FFT Intensities and Expression Values

The intensity values as determined from smooth periodograms (FFT) in thecontext of gene expression for the 120-280 bp range were analyzed. AnS-shaped Pearson correlation between gene expression values and FFTintensities around the major inter-nucleosome distance peak wasobserved. A pronounced negative correlation was observed in the 193-199bp range. As a result, the intensities in this frequency range wereaveraged correlated with log₂-transformed expression values.

Further Examples

Example 7. A method of determining tissues and/or cell types giving riseto cell free DNA (cfDNA) in a subject, the method comprising:

-   isolating cfDNA from a biological sample from the subject, the    isolated cfDNA comprising a plurality of cfDNA fragments;-   determining a sequence associated with at least a portion of the    plurality of cfDNA fragments;-   determining a genomic location within a reference genome for at    least some cfDNA fragment endpoints of the plurality of cfDNA    fragments as a function of the cfDNA fragment sequences; and-   determining at least some of the tissues and/or cell types giving    rise to the cfDNA fragments as a function of the genomic locations    of at least some of the cfDNA fragment endpoints.

Example 8. The method of Example 7 wherein the step of determining atleast some of the tissues and/or cell types giving rise to the cfDNAfragments comprises comparing the genomic locations of at least some ofthe cfDNA fragment endpoints to one or more reference maps.

Example 9. The method of Example 7 or Example 8 wherein the step ofdetermining at least some of the tissues and/or cell types giving riseto the cfDNA fragments comprises performing a mathematicaltransformation on a distribution of the genomic locations of at leastsome of the cfDNA fragment endpoints.

Example 10. The method of Example 9 wherein the mathematicaltransformation includes a Fourier transformation.

Example 11. The method of any preceding Example further comprisingdetermining a score for each of at least some coordinates of thereference genome, wherein the score is determined as a function of atleast the plurality of cfDNA fragment endpoints and their genomiclocations, and wherein the step of determining at least some of thetissues and/or cell types giving rise to the observed cfDNA fragmentscomprises comparing the scores to one or more reference map.

Example 12. The method of Example 11, wherein the score for a coordinaterepresents or is related to the probability that the coordinate is alocation of a cfDNA fragment endpoint.

Example 13. The method of any one of Examples 8 to 12 wherein thereference map comprises a DNase I hypersensitive site map generated fromat least one cell-type or tissue.

Example 14. The method of any one of Examples 8 to 13 wherein thereference map comprises an RNA expression map generated from at leastone cell-type or tissue.

Example 15. The method of any one of Examples 8 to 14 wherein thereference map is generated from cfDNA from an animal to which humantissues or cells that have been xenografted.

Example 16. The method of any one of Examples 8 to 15 wherein thereference map comprises a chromosome conformation map generated from atleast one cell-type or tissue.

Example 17. The method of any one of Examples 8 to 16 wherein thereference map comprises a chromatin accessibility map generated from atleast one cell-type or tissue.

Example 18. The method of any one of Examples 8 to 17 wherein thereference map comprises sequence data obtained from samples obtainedfrom at least one reference subject.

Example 19. The method of any one of Examples 8 to 18 wherein thereference map corresponds to at least one cell-type or tissue that isassociated with a disease or a disorder.

Example 20. The method of any one of Examples 8 to 19 wherein thereference map comprises positions or spacing of nucleosomes and/orchromatosomes in a tissue or cell type.

Example 21. The method of any one of Examples 8 to 20 wherein thereference map is generated by digesting chromatin obtained from at leastone cell-type or tissue with an exogenous nuclease (e.g., micrococcalnuclease).

Example 22. The method of any one of Examples 8 to 21, wherein thereference maps comprise chromatin accessibility data determined by atransposition-based method (e.g., ATAC-seq) from at least one cell-typeor tissue.

Example 23. The method of any one of Examples 8 to 22 wherein thereference maps comprise data associated with positions of a DNA bindingand/or DNA occupying protein for a tissue or cell type.

Example 24. The method of Example 23 wherein the DNA binding and/or DNAoccupying protein is a transcription factor.

Example 25. The method of Example 23 or Example 24 wherein the positionsare determined by chromatin immunoprecipitation of a crosslinkedDNA-protein complex.

Example 26. The method of Example 23 or Example 24 wherein the positionsare determined by treating DNA associated with the tissue or cell typewith a nuclease (e.g., DNase-I).

Example 27. The method of any one of Examples 8 to 26 wherein thereference map comprises a biological feature related to the positions orspacing of nucleosomes, chromatosomes, or other DNA binding or DNAoccupying proteins within a tissue or cell type.

Example 28. The method of Example 27 wherein the biological feature isquantitative expression of one or more genes.

Example 29. The method of Example 27 or Example 28 wherein thebiological feature is presence or absence of one or more histone marks.

Example 30. The method of any one of Examples 27 to 29 wherein thebiological feature is hypersensitivity to nuclease cleavage.

Example 31. The method of any one of Examples 8 to 30 wherein the tissueor cell type used to generate a reference map is a primary tissue from asubject having a disease or disorder.

Example 32. The method of Example 31 wherein the disease or disorder isselected from the group consisting of: cancer, normal pregnancy, acomplication of pregnancy (e.g., aneuploid pregnancy), myocardialinfarction, inflammatory bowel disease, systemic autoimmune disease,localized autoimmune disease, allotransplantation with rejection,allotransplantation without rejection, stroke, and localized tissuedamage.

Example 33. The method of any one of Examples 8 to 30 wherein the tissueor cell type used to generate a reference map is a primary tissue from ahealthy subject.

Example 34. The method of any one of Examples 8 to 30 wherein the tissueor cell type used to generate a reference map is an immortalized cellline.

Example 35. The method of any one of Examples 8 to 30 wherein the tissueor cell type used to generate a reference map is a biopsy from a tumor.

Example 36. The method of Example 18 wherein the sequence data comprisespositions of cfDNA fragment endpoints.

Example 37. The method of Example 36 wherein the reference subject ishealthy.

Example 38. The method of Example 36 wherein the reference subject has adisease or disorder.

Example 39. The method of Example 38 wherein the disease or disorder isselected from the group consisting of: cancer, normal pregnancy, acomplication of pregnancy (e.g., aneuploid pregnancy), myocardialinfarction, inflammatory bowel disease, systemic autoimmune disease,localized autoimmune disease, allotransplantation with rejection,allotransplantation without rejection, stroke, and localized tissuedamage.

Example 40. The method of any one of Examples 19 to 39 wherein thereference map comprises reference scores for at least a portion ofcoordinates of the reference genome associated with the tissue or celltype.

Example 41. The method of Example 40 wherein the reference map comprisesa mathematical transformation of the scores.

Example 42. The method of Example 40 wherein the scores represent asubset of all reference genomic coordinates for the tissue or cell type.

Example 43. The method of Example 42 wherein the subset is associatedwith positions or spacing of nucleosomes and/or chromatosomes.

Example 44. The method of Example 42 or Example 43 wherein the subset isassociated with transcription start sites and/or transcription endsites.

Example 45. The method of any one of Examples 42 to 44 wherein thesubset is associated with binding sites of at least one transcriptionfactor.

Example 46. The method of any one of Examples 42 to 45 wherein thesubset is associated with nuclease hypersensitive sites.

Example 47. The method of any one of Examples 40 to 46 wherein thesubset is additionally associated with at least one orthogonalbiological feature.

Example 48. The method of Example 47 wherein the orthogonal biologicalfeature is associated with high expression genes.

Example 49. The method of Example 47 wherein the orthogonal biologicalfeature is associated with low expression genes.

Example 50. The method of any one of Examples 41 to 49 wherein themathematical transformation includes a Fourier transformation.

Example 51. The method of any one of Examples 11 to 50 wherein at leasta subset of the plurality of the scores has a score above a thresholdvalue.

Example 52. The method of any one of Examples 7 to 51 wherein the stepof determining the tissues and/or cell types giving rise to the cfDNA asa function of a plurality of the genomic locations of at least some ofthe cfDNA fragment endpoints comprises comparing a Fourier transform ofthe plurality of the genomic locations of at least some of the cfDNAfragment endpoints, or a mathematical transformation thereof, with areference map.

Example 53. The method of any preceding Example further comprisinggenerating a report comprising a list of the determined tissues and/orcell types giving rise to the isolated cfDNA.

Example 54. A method of identifying a disease or disorder in a subject,the method comprising:

-   isolating cell free DNA (cfDNA) from a biological sample from the    subject, the isolated cfDNA comprising a plurality of cfDNA    fragments;-   determining a sequence associated with at least a portion of the    plurality of cfDNA fragments;-   determining a genomic location within a reference genome for at    least some cfDNA fragment endpoints of the plurality of cfDNA    fragments as a function of the cfDNA fragment sequences;-   determining at least some of the tissues and/or cell types giving    rise to the cfDNA as a function of the genomic locations of at least    some of the cfDNA fragment endpoints; and-   identifying the disease or disorder as a function of the determined    tissues and/or cell types giving rise to the cfDNA.

Example 55. The method of Example 54 wherein the step of determining thetissues and/or cell types giving rise to the cfDNA comprises comparingthe genomic locations of at least some of the cfDNA fragment endpointsto one or more reference maps.

Example 56. The method of Example 54 or Example 55 wherein the step ofdetermining the tissues and/or cell types giving rise to the cfDNAcomprises performing a mathematical transformation on a distribution ofthe genomic locations of at least some of the plurality of the cfDNAfragment endpoints.

Example 57. The method of Example 56 wherein the mathematicaltransformation includes a Fourier transformation.

Example 58. The method of any one of Examples 54 to 57 furthercomprising determining a score for each of at least some coordinates ofthe reference genome, wherein the score is determined as a function ofat least the plurality of cfDNA fragment endpoints and their genomiclocations, and wherein the step of determining at least some of thetissues and/or cell types giving rise to the observed cfDNA fragmentscomprises comparing the scores to one or more reference map.

Example 59. The method of Example 58, wherein the score for a coordinaterepresents or is related to the probability that the coordinate is alocation of a cfDNA fragment endpoint.

Example 60. The method of any one of Examples 55 to 59 wherein thereference map comprises a DNase I hypersensitive site map, an RNAexpression map, expression data, a chromosome conformation map, achromatin accessibility map, chromatin fragmentation map, or sequencedata obtained from samples obtained from at least one reference subject,and corresponding to at least one cell type or tissue that is associatedwith a disease or a disorder, and/or positions or spacing of nucleosomesand/or chromatosomes in a tissue or cell type.

Example 61. The method of any one of Examples 55 to 60 wherein thereference map is generated by digesting chromatin from at least onecell-type or tissue with an exogenous nuclease (e.g., micrococcalnuclease).

Example 62. The method of Example 60 or Example 61, wherein thereference maps comprise chromatin accessibility data determined byapplying a transposition-based method (e.g., ATAC-seq) to nuclei orchromatin from at least one cell-type or tissue.

Example 63. The method of any one of Examples 55 to 62 wherein thereference maps comprise data associated with positions of a DNA bindingand/or DNA occupying protein for a tissue or cell type.

Example 64. The method of Example 63 wherein the DNA binding and/or DNAoccupying protein is a transcription factor.

Example 65. The method of Example 63 or Example 64 wherein the positionsare determined by applying chromatin immunoprecipitation of acrosslinked DNA-protein complex to at least one cell-type or tissue.

Example 66. The method of Example 63 or Example 64 wherein the positionsare determined by treating DNA associated with the tissue or cell typewith a nuclease (e.g., DNase-I).

Example 67. The method of any one of Examples 54 to 66 wherein thereference map comprises a biological feature related to the positions orspacing of nucleosomes, chromatosomes, or other DNA binding or DNAoccupying proteins within a tissue or cell type.

Example 68. The method of Example 67 wherein the biological feature isquantitative expression of one or more genes.

Example 69. The method of Example 67 or Example 68 wherein thebiological feature is presence or absence of one or more histone marks.

Example 70. The method of Example any one of Examples 67 to 69 whereinthe biological feature is hypersensitivity to nuclease cleavage.

Example 71. The method of any one of Examples 55 to 70 wherein thetissue or cell type used to generate a reference map is a primary tissuefrom a subject having a disease or disorder.

Example 72. The method of Example 71 wherein the disease or disorder isselected from the group consisting of: cancer, normal pregnancy, acomplication of pregnancy (e.g., aneuploid pregnancy), myocardialinfarction, inflammatory bowel disease, systemic autoimmune disease,localized autoimmune disease, allotransplantation with rejection,allotransplantation without rejection, stroke, and localized tissuedamage.

Example 73. The method of any one of Examples 55 to 70 wherein thetissue or cell type used to generate a reference map is a primary tissuefrom a healthy subject.

Example 74. The method of any one of Examples 55 to 70 wherein thetissue or cell type used to generate a reference map is an immortalizedcell line.

Example 75. The method of any one of Examples 55 to 70 wherein thetissue or cell type used to generate a reference map is a biopsy from atumor.

Example 76. The method of Example 60 wherein the sequence data obtainedfrom samples obtained from at least one reference subject comprisespositions of cfDNA fragment endpoint probabilities.

Example 77. The method of Example 76 wherein the reference subject ishealthy.

Example 78. The method of Example 76 wherein the reference subject has adisease or disorder.

Example 79. The method of Example 78 wherein the disease or disorder isselected from the group consisting of: cancer, normal pregnancy, acomplication of pregnancy (e.g., aneuploid pregnancy), myocardialinfarction, inflammatory bowel disease, systemic autoimmune disease,localized autoimmune disease, allotransplantation with rejection,allotransplantation without rejection, stroke, and localized tissuedamage.

Example 80. The method of any one of Examples 60 to 79 wherein thereference map comprises cfDNA fragment endpoint probabilities for atleast a portion of the reference genome associated with the tissue orcell type.

Example 81. The method of Example 80 wherein the reference map comprisesa mathematical transformation of the cfDNA fragment endpointprobabilities.

Example 82. The method of Example 80 wherein the cfDNA fragment endpointprobabilities represent a subset of all reference genomic coordinatesfor the tissue or cell type.

Example 83. The method of Example 82 wherein the subset is associatedwith positions or spacing of nucleosomes and/or chromatosomes.

Example 84. The method of Example 82 or Example 83 wherein the subset isassociated with transcription start sites and/or transcription endsites.

Example 85. The method of any one of Examples 82 to 84 wherein thesubset is associated with binding sites of at least one transcriptionfactor.

Example 86. The method of any one of Examples 82 to 85 wherein thesubset is associated with nuclease hypersensitive sites.

Example 87. The method of any one of Examples 82 to 86 wherein thesubset is additionally associated with at least one orthogonalbiological feature.

Example 88. The method of Example 87 wherein the orthogonal biologicalfeature is associated with high expression genes.

Example 89. The method of Example 87 wherein the orthogonal biologicalfeature is associated with low expression genes.

Example 90. The method of any one of Examples 81 to 89 wherein themathematical transformation includes a Fourier transformation.

Example 91. The method of any one of Examples 58 to 90 wherein at leasta subset of the plurality of the cfDNA fragment endpoint scores each hasa score above a threshold value.

Example 92. The method of any one of Examples 54 to 91 wherein the stepof determining the tissue(s) and/or cell type(s) of the cfDNA as afunction of a plurality of the genomic locations of at least some of thecfDNA fragment endpoints comprises comparing a Fourier transform of theplurality of the genomic locations of at least some of the cfDNAfragment endpoints, or a mathematical transformation thereof, with areference map.

Example 93. The method of any one of Examples 54 to 92 wherein thereference map comprises DNA or chromatin fragmentation datacorresponding to at least one tissue that is associated with the diseaseor disorder.

Example 94. The method of any one of Examples 54 to 93 wherein thereference genome is associated with a human.

Example 95. The method of any one of Examples 54 to 94 furthercomprising generating a report comprising a statement identifying thedisease or disorder.

Example 96. The method of Example 95 wherein the report furthercomprises a list of the determined tissue(s) and/or cell type(s) of theisolated cfDNA.

Example 97. The method of any preceding Example wherein the biologicalsample comprises, consists essentially of, or consists of whole blood,peripheral blood plasma, urine, or cerebral spinal fluid.

Example 98. A method for determining tissues and/or cell types givingrise to cell-free DNA (cfDNA) in a subject, comprising:

-   (i) generating a nucleosome map by obtaining a biological sample    from the subject, isolating cfDNA from the biological sample, and    measuring distributions (a), (b) and/or (c) by library construction    and massively parallel sequencing of cfDNA;-   (ii) generating a reference set of nucleosome maps by obtaining a    biological sample from control subjects or subjects with known    disease, isolating the cfDNA from the biological sample, measuring    distributions (a), (b) and/or (c) by library construction and    massively parallel sequencing of cfDNA; and-   (iii) determining the tissues and/or cell types giving rise to the    cfDNA by comparing the nucleosome map derived from the cfDNA to the    reference set of nucleosome maps;

wherein (a), (b) and (c) are:

-   (a) the distribution of likelihoods any specific base-pair in a    human genome will appear at a terminus of a cfDNA fragment;-   (b) the distribution of likelihoods that any pair of base-pairs of a    human genome will appear as a pair of termini of a cfDNA fragment;    and-   (c) the distribution of likelihoods that any specific base-pair in a    human genome will appear in a cfDNA fragment as a consequence of    differential nucleosome occupancy.

Example 99. A method for determining tissues and/or cell types givingrise to cell-free DNA in a subject, comprising:

-   (i) generating a nucleosome map by obtaining a biological sample    from the subject, isolating the cfDNA from the biological sample,    and measuring distributions (a), (b) and/or (c) by library    construction and massively parallel sequencing of cfDNA;-   (ii) generating a reference set of nucleosome maps by obtaining a    biological sample from control subjects or subjects with known    disease, isolating the cfDNA from the biological sample, measuring    distributions (a), (b) and/or (c) by library construction and    massively parallel sequencing of DNA derived from digestion of    chromatin with micrococcal nuclease (MNase), DNase treatment, or    ATAC-Seq; and-   (iii) determining the tissues and/or cell types giving rise to the    cfDNA by comparing the nucleosome map derived from the cfDNA to the    reference set of nucleosome maps;

wherein (a), (b) and (c) are:

-   (a) the distribution of likelihoods any specific base-pair in a    human genome will appear at a terminus of a sequenced fragment;-   (b) the distribution of likelihoods that any pair of base-pairs of a    human genome will appear as a pair of termini of a sequenced    fragment; and-   (c) the distribution of likelihoods that any specific base-pair in a    human genome will appear in a sequenced fragment as a consequence of    differential nucleosome occupancy.

Example 100. A method for diagnosing a clinical condition in a subject,comprising:

-   (i) generating a nucleosome map by obtaining a biological sample    from the subject, isolating cfDNA from the biological sample, and    measuring distributions (a), (b) and/or (c) by library construction    and massively parallel sequencing of cfDNA;-   (ii) generating a reference set of nucleosome maps by obtaining a    biological sample from control subjects or subjects with known    disease, isolating the cfDNA from the biological sample, measuring    distributions (a), (b) and/or (c) by library construction and    massively parallel sequencing of cfDNA; and-   (iii) determining the clinical condition by comparing the nucleosome    map derived from the cfDNA to the reference set of nucleosome maps;

wherein (a), (b) and (c) are:

-   (a) the distribution of likelihoods any specific base-pair in a    human genome will appear at a terminus of a cfDNA fragment;-   (b) the distribution of likelihoods that any pair of base-pairs of a    human genome will appear as a pair of termini of a cfDNA fragment;    and-   (c) the distribution of likelihoods that any specific base-pair in a    human genome will appear in a cfDNA fragment as a consequence of    differential nucleosome occupancy.

Example 101. A method for diagnosing a clinical condition in a subject,comprising

-   (i) generating a nucleosome map by obtaining a biological sample    from the subject, isolating cfDNA from the biological sample, and    measuring distributions (a), (b) and/or (c) by library construction    and massively parallel sequencing of cfDNA;-   (ii) generating a reference set of nucleosome maps by obtaining a    biological sample from control subjects or subjects with known    disease, isolating the cfDNA from the biological sample, measuring    distributions (a), (b) and/or (c) by library construction and    massively parallel sequencing of DNA derived from digestion of    chromatin with micrococcal nuclease (MNase), DNase treatment, or    ATAC-Seq; and-   (iii) determining the tissue-of-origin composition of the cfDNA by    comparing the nucleosome map derived from the cfDNA to the reference    set of nucleosome maps;

wherein (a), (b) and (c) are:

-   (a) the distribution of likelihoods any specific base-pair in a    human genome will appear at a terminus of a sequenced fragment;-   (b) the distribution of likelihoods that any pair of base-pairs of a    human genome will appear as a pair of termini of a sequenced    fragment; and-   (c) the distribution of likelihoods that any specific base-pair in a    human genome will appear in a sequenced fragment as a consequence of    differential nucleosome occupancy.

Example 102. The method of any one of Examples 98-101, wherein thenucleosome map is generated by:

-   purifying the cfDNA isolated from the biological sample;-   constructing a library by adaptor ligation and optionally PCR    amplification; and-   sequencing the resulting library.

Example 103. The method of any one of Examples 98-101, wherein thereference set of nucleosome maps are generated by:

-   purifying cfDNA isolated from the biological sample from control    subjects;-   constructing a library by adaptor ligation and optionally PCR    amplification; and-   sequencing the resulting library.

Example 104. The method of any one of Examples 98-101, whereindistribution (a), (b) or (c), or a mathematical transformation of one ofthese distributions, is subjected to Fourier transformation incontiguous windows, followed by quantitation of intensities forfrequency ranges that are associated with nucleosome occupancy, in orderto summarize the extent to which nucleosomes exhibit structuredpositioning within each contiguous window.

Example 105. The method of any one of Examples 98-101, wherein indistribution (a), (b) or (c), or a mathematical transformation of one ofthese distributions, we quantify the distribution of sites in thereference human genome to which sequencing read start sites map in theimmediate vicinity of transcription factor binding sites (TFBS) ofspecific transcription factor (TF), which are often immediately flankedby nucleosomes when the TFBS is bound by the TF, in order to summarizenucleosome positioning as a consequence of TF activity in the celltype(s) contributing to cfDNA.

Example 106. The method of any one of Examples 98-101, wherein thenucleosome occupancy signals are summarized in accordance with any oneof aggregating signal from distributions (a), (b), and/or (c), or amathematical transformation of one of these distributions, around othergenomic landmarks such as DNasel hypersensitive sites, transcriptionstart sites, topological domains, other epigenetic marks or subsets ofall such sites defined by correlated behavior in other datasets (e.g.gene expression, etc.).

Example 107. The method of any one of Examples 98-101, wherein thedistributions are transformed in order to aggregate or summarize theperiodic signal of nucleosome positioning within various subsets of thegenome, e.g. quantifying periodicity in contiguous windows or,alternatively, in discontiguous subsets of the genome defined bytranscription factor binding sites, gene model features (e.g.transcription start sites), tissue expression data or other correlatesof nucleosome positioning.

Example 108. The method of any one of Examples 98-101, wherein thedistributions are defined by tissue-specific data, i.e. aggregate signalin the vicinity of tissue-specific DNase I hypersensitive sites.

Example 109. The method of any one of Examples 98-101, furthercomprising step of statistical signal processing for comparingadditional nucleosome map(s) to the reference set.

Example 110. The method of Example 109, wherein we first summarizelong-range nucleosome ordering within contiguous windows along thegenome in a diverse set of samples, and then perform principalcomponents analysis (PCA) to cluster samples or to estimate mixtureproportions.

Example 111. The method of Example 100 or Example 101, wherein theclinical condition is cancer, i.e. malignancies.

Example 112. The method of Example 111, wherein the biological sample iscirculating plasma containing cfDNA, some portion of which is derivedfrom a tumor.

Example 113. The method of Example 100 or Example 101, wherein theclinical condition is selected from tissue damage, myocardial infarction(acute damage of heart tissue), autoimmune disease (chronic damage ofdiverse tissues), pregnancy, chromosomal aberrations (e.g. trisomies),and transplant rejection.

Example 114. The method of any preceding Example further comprisingassigning a proportion to each of the one or more tissues or cell typesdetermined to be contributing to cfDNA.

Example 115. The method of Example 114 wherein the proportion assignedto each of the one or more determined tissues or cell types is based atleast in part on a degree of correlation or of increased correlation,relative to cfDNA from a healthy subject or subjects.

Example 116. The method of Example 114 or Example 115, wherein thedegree of correlation is based at least in part on a comparison of amathematical transformation of the distribution of cfDNA fragmentendpoints from the biological sample with the reference map associatedwith the determined tissue or cell type.

Example 117 The method of Example 114 to 116, wherein the proportionassigned to each of the one or more determined tissues or cell types isbased on a mixture model.

From the foregoing, it will be appreciated that specific embodiments ofthe invention have been described herein for purposes of illustration,but that various modifications may be made without deviating from thescope of the invention. Accordingly, the invention is not limited exceptas by the appended claims.

1. A method of identifying a clinical condition in a test subject, themethod comprising: (a) obtaining sequences of at least a portion ofcfDNA present in a test sample from the test subject, wherein thesequences have endpoints and the sequences are mappable to genomiclocations; (b) calculating a test sample value of the frequency ofsample cfDNA endpoints at one or more genomic locations, wherein thefrequency of the sample cfDNA endpoints is a function of the number ofendpoints and the number of sequenced molecules mappable to the genomiclocations; (c) comparing the test sample value with a first referencevalue calculated from the frequency of cfDNA endpoints obtained from thesequences of cfDNA molecules obtained from a first reference sample,wherein the frequency of the cfDNA endpoints is a function of the numberof endpoints and the number of sequenced molecules mappable to thegenomic locations; wherein the first reference sample is obtained from aperson known to have the presence or the absence of the clinicalcondition, and (d) identifying or diagnosing the presence or absence ofthe clinical condition based on at least the comparing step.
 2. Themethod of claim 1, wherein the first reference sample is obtained from aperson known to have the clinical condition.
 3. The method of claim 1,wherein the first reference sample is obtained from a person known tonot have the clinical condition.
 4. The method of claim 2, furthercomprising the steps of: comparing the value of frequency of the samplecfDNA endpoints with a second reference value calculated from the cfDNAendpoints obtained from the sequences of cfDNA obtained from a secondreference sample and wherein the frequency of the endpoints are afunction of the number of endpoints and the number of sequencedmolecules mappable to the defined genomic location, wherein the secondreference sample is obtained from a person known to not have theclinical condition.
 5. The method of claim 4, wherein identifying ordiagnosing the presence or absence of the clinical condition comprisesdetermining if the test sample value is more similar to the firstreference value or the second reference value.
 6. The method of claim 5,wherein the clinical condition is cancer.
 7. The method of claim 5,wherein the genomic location comprises a transcription factor bindingsite.
 8. The method of claim 5, wherein the genomic location comprises aCTCF binding site.
 9. The method of claim 6, wherein the cfDNA sequencedcomprises DNA derived from hematopoietic cells and non-hematopoieticcells, wherein at least a portion of the non-hematopoietic cells aretumor cells.
 10. The method of claim 8, wherein test values andreference values are calculated for a plurality of genomic locations.11. The method of claim 8, wherein the cfDNA is sequenced on a massivelyparallel DNA sequencer.
 12. The method of claim 5, wherein the genomiclocations comprise a plurality of separate genomic locations andseparate test values and reference values for each genomic location. 13.The method of claim 12, wherein test frequency values and the referencefrequency values are compared for a plurality of genomic locations. 14.The method of claim 8, wherein the test frequency values and thereference frequency values are calculated at a single genomic coordinateresolution level.
 15. The method of claim 5, wherein test frequencyvalues and the reference frequency values are calculated at a singlegenomic coordinate resolution for a plurality of single base locationswithin the genomic locations.
 16. The method of claim 14, wherein testfrequency values and reference frequency values are calculated at asingle genomic coordinate resolution for a plurality of single baselocations within the genomic locations.
 17. The method of claim 15,wherein test frequency values and reference frequency values arecompared for a plurality of genomic locations.
 18. The method of claim16, wherein test frequency values and reference frequency values arecompared for a plurality of genomic locations.